Calibration of pulse-transit-time to blood pressure model using multiple physiological sensors and various methods for blood pressure variation

ABSTRACT

Disclosed are devices and methods for estimating blood pressure, which implement a pulse-transit-time-based blood pressure model that can be calibrated. Some implementations provide reliable and user friendly means for calibrating the blood pressure model using blood pressure perturbation methods and multiple sensors.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims benefits under 35 U.S.C. §119(e) of U.S.Provisional Patent Application No. 62/286,876, entitled: CALIBRATION OFPULSE-TRANSIT-TIME TO BLOOD PRESSURE MODEL USING MULTIPLE PHYSIOLOGICALSENSORS AND VARIOUS METHODS FOR BLOOD PRESSURE VARIATION, filed Jan. 25,2016, which is herein incorporated by reference in its entirety for allpurposes.

INTRODUCTION

This disclosure provides devices and methods for estimating bloodpressure, which implement a pulse transit time based blood pressuremodel that can be calibrated.

Blood pressure (BP) is an important indicator of cardiovascular healthand other physiological conditions. Blood pressure may be measured withconventional methods on an infrequent basis in a medical setting.However, there are also needs to measure blood pressure on a morefrequent basis and under various conditions outside of the medicalsetting. For instance, certain blood pressure dynamics, e.g., thenon-dipper blood pressure pattern, when a person is sleeping, are ofmedical import. Such dynamics are not easily measurable withconventional methods. Moreover, for example, blood pressure variationsduring daily activities after certain medications are consumed may beused to optimize medical treatments.

Conventional noninvasive methods for measuring blood pressure includeoscillometric blood pressure measurement (OBPM) methods and auscultationmethods using sphygmomanometer. Both methods require the use of aninflating cuff which can cause discomfort to the user. Otherconventional blood pressure measuring methods include tonometry and thevolume clamp method. The former requires a trained operator while thelatter is also cumbersome and relatively obtrusive in a person's everyday routine. As such, these conventional methods are not ideal for bloodpressure measurements on a frequent basis and under various conditions,e.g., ambulatory blood pressure measurements (ABPM).

In the recent years pulse transit time (PTT) has emerged as a promisingalternative for continuous, unobtrusive blood pressure measurement. Themain principle is that pulse wave velocity (PWV) or distance divided byPTT is proportional to blood pressure, i.e. as blood pressure increasesthe speed at which the pressure wave travels through the arteriesincreases.

An issue that exists with PTT is reliable calibration for a specificuser. Since a person's blood pressure values have a limited range in acalibration period, it becomes particularly challenging to acquire goodtraining data to train a PTT-to-BP model.

It is to be appreciated that the Inventors of the embodiments describedherein have identified the need for efficient blood pressuremeasurements that can be performed on a frequent basis and under variousconditions. Furthermore, the Inventors have identified that means forreliable or convenient BP-to-PTT calibration would be useful forimproving the accuracy or convenience of PTT-based blood pressuremeasuring devices.

SUMMARY

Devices and methods are provided for continuous, unobtrusive bloodpressure measurement. In some implementations, wearable biometricmonitoring devices for measuring blood pressure using PTT may be used toobtain blood pressure measurements when the user is engaged in dailyactivities.

One aspect of the disclosure relates to methods for calibrating a bloodpressure measuring system. In some implementations, the system includesa calibration blood pressure measuring device, one or more sensorsconfigured to measure pulse transit time, a memory, and one or moreprocessors. The calibration blood pressure measuring device does notrely on pulse transit time to measure the blood pressure. The methodinvolves: (a) taking at least one measurement of a blood pressure of aperson with the calibration blood pressure device when the person isperforming a Valsalva maneuver; (b) obtaining proximal pulse wave dataand distal pulse wave data from the one or more sensors when the personis performing the Valsalva maneuver; (c) obtaining, by using the one ormore processors, at least one pulse transit time using the proximalpulse wave data and the distal pulse wave data obtained when the personis performing the Valsalva maneuver; (d) obtaining, by using the one ormore processors, at least one calibration data point corresponding tothe at least one measurement of the blood pressure and the at least onepulse transit time; and (e) fitting, by using the one or moreprocessors, a model to two or more data points comprising the at leastone calibration data point, wherein the model relates the blood pressureof the person to the pulse transit time of the person.

In some implementations, the method further involves: obtaining PPG datafrom the person during a time period when the Valsalva maneuver isperformed; and initiating (a) and (b) based on determining, by the oneor more processors, that an amplitude of the PPG data has decreased inthe time period by more than a threshold value.

In some implementations, the method further involves: measuring heartrate of the person during a time period when the Valsalva maneuver isperformed; and initiating (a) and (b) based on determining, by the oneor more processors, that the heart rate of the person has increased inthe time period by more than a threshold value. In some implementations,the method further includes: receiving a user request to calibrate thesystem; and initiating (a) and (b) based on the user request. In someimplementations, the method further includes: repeating (a)-(d) one ormore times. In some implementations, the model includes a linearmathematical relationship, a general linear model, a non-linear model,or a neural network model.

In some implementations, the two or more data points of (d) comprise abaseline data point. In some implementations, the baseline data pointcorresponds to: a baseline measurement of a blood pressure of the personobtained when the person is in a baseline state; and a baseline pulsetransit time obtained when the person is in the baseline state.

In some implementations, the method further including, before (a),providing instructions to the person to instruct the person to initiatea calibration of the system. In some implementations, the instructionsinclude instructing the person to perform a Valsalva maneuver. In someimplementations, the instructions are provided based on a time schedule.In some implementations, the instructions are provided based on a usercontext. In some implementations, the user context is selected from oneor more of the following: the user was at a restaurant, time isapproaching the time that the user typically goes to sleep, the userjust exercised, the user just woke up, the user just ended a commute,significant change has occurred in perfusion properties when stationary,the hand to which one of the sensors is attached is at approximately thesame orientation as before, and any combinations thereof.

In some implementations, the method further includes: receiving a userrequest to calibrate the system; and providing the instructions to theperson to instruct the person to initiate the calibration based on theuser request.

In some implementations, the method further involves, after (e):obtaining a test proximal pulse wave data and a test distal pulse wavedata from the person; obtaining a test pulse transit time or a valuederived therefrom using the test proximal pulse wave data and the testdistal pulse wave data; and applying the test pulse transit time or thevalue derived therefrom to the model to obtain an estimate of bloodpressure.

Another aspect of the disclosure relates to additional methods forcalibrating a blood pressure measuring system. In some implementations,the system includes a calibration blood pressure measuring device, oneor more sensors configured to measure pulse transit time, a temperaturesensor, a memory, and one or more processors. The calibration bloodpressure measuring device does not rely on pulse transit time to measurethe blood pressure. The method involves: (a) taking at least onemeasurement of a blood pressure of a person with the calibration bloodpressure device when the person is performing the cold pressor maneuver;(b) obtaining proximal pulse wave data and distal pulse wave data fromthe one or more sensors when the person is performing the cold pressormaneuver; (c) obtaining, by using the one or more processors, at leastone pulse transit time using the proximal pulse wave data and the distalpulse wave data obtained when the person is performing the cold pressormaneuver; (d) obtaining, by using the one or more processors, at leastone calibration data point corresponding to the at least one measurementof the blood pressure and the at least one pulse transit time; and (e)fitting, by using the one or more processors, a model to two or moredata points including the at least one calibration data point, whereinthe model relates the blood pressure of the person to the pulse transittime of the person.

In some implementations, the method further includes: repeating (a)-(d)one or more times. In some implementations, the two or more data pointsof (d) include a baseline data point. In some implementations, thebaseline data point includes: a baseline measurement of a blood pressureof the person obtained when the person is in a baseline state, or avalue derived therefrom; and a baseline pulse transit time obtained whenthe person is in the baseline state, or a value derived therefrom.

In some implementations, the method further involves, before (a):measuring temperature at a position of a person using the temperaturesensor in a time period when the person is performing a cold pressormaneuver; determining, by the one or more processors, that thetemperature at the position meets a criterion; and automaticallytriggering (a) based on the determination that the temperature meets thecriterion.

A further aspect of the disclosure relates to additional methods forcalibrating a blood pressure measuring system. The system includes analtimeter, a calibration blood pressure device for measuring bloodpressure, one or more sensors for measuring pulse transit time, amemory, and one or more processors. In some implementations, the methodinvolves: (a) obtaining, by the one or more processors, a measurement ofblood pressure of the person with the blood pressure device and a set ofaltitude data from the altimeter when the person is holding a limb at aposition with the system attached to the limb, and wherein thecalibration blood pressure device does not rely on pulse transit time tomeasure blood pressure; (b) obtaining, by the one or more processors, anestimate of hydrostatic pressure from the set of altitude data; (c)obtaining proximal pulse wave data and distal pulse wave data by usingthe one or more sensors when the person is holding the limb at the firstposition; (d) obtaining, by the one or more processors, a pulse transittime using the proximal pulse wave data and the distal pulse wave data;(e) repeating (a)-(d) one or more times when the person is holding thelimb at one or more different positions, thereby obtaining two or morecalibration data points corresponding to two or more measurements ofblood pressure, two or more pulse transit times, and two or moremeasurements of hydrostatic pressure; and (f) fitting, by the controllogic, a model to data points including the two or more calibration datapoints, wherein the model relates blood pressure to pulse transit timeand hydrostatic pressure.

In some implementations, the system further includes an inertial sensor,and the method further includes obtaining a set of inertial data fromthe inertial sensor when the person is holding the limb at the position.In some implementations, (b) includes obtaining the estimate ofhydrostatic pressure from the set of inertial data and the set ofaltitude data. In some implementations, the inertial sensor is selectedfrom the group consisting of an accelerometer, a gyroscope, amagnetometer, and any combinations thereof. In some implementations, thealtimeter, the inertial sensor, and the calibration blood pressuredevice are attached to a same area of the limb when the measurement ofblood pressure is obtained.

Another aspect of the disclosure relates to methods for calibrating ablood pressure measuring system including an inflatable cuff. The systemalso includes a calibration blood pressure device for measuring bloodpressure, one or more sensors for measuring pulse transit time, amemory, and one or more processors. The method involves: (a) applying anexternal pressure to a person using the inflatable cuff; (b) obtaining ameasurement of internal blood pressure of a person with the calibrationblood pressure device, wherein the calibration blood pressure devicedoes not rely on pulse transit time to measure the blood pressure; (c)obtaining a transmural blood pressure level from the measurement ofinternal blood pressure and the external pressure; (d) obtainingproximal pulse wave data and distal pulse wave data from the one or moresensors; (e) obtaining a pulse transit time using the proximal pulsewave data and the distal pulse wave data; (f) repeating (a)-(e) one ormore times when applying one or more different external pressures,thereby obtaining two or more calibration data points corresponding totwo or more transmural blood pressure levels and two or more pulsetransit times; and (g) fitting a model to data points including the twoor more calibration data points, wherein the model relates transmuralblood pressure to pulse transit time.

In some implementations, the inflatable cuff includes a pressure sensor,and the method further includes determining the external pressure usingthe pressure sensor. In some implementations, the method furtherincludes: integrating an effect of the external pressure based on alength of the arm segment that is compressed by the inflatable cuff. Insome implementations, the method further includes normalizing theintegrated effect by a total length that a pulse wave travels. Anadditional aspect of the disclosure relates to a system for measuringblood pressure. The system includes: a weight scale including at leastone first biometric sensor; and a wrist-worn device including at leastone second biometric sensor. The weight scale and the wrist-worn deviceare communicatively linked, and the weight scale or the wrist worndevice includes one or more processors configured to estimate bloodpressure using data obtained from the at least one first biometricsensor and the at least one second biometric sensor.

In some implementations, the at least one first biometric sensor isselected from the group consisting of: a photoplethysmorgraphy (PPG)sensor, an ECG sensor, a phonocardiography (PCG) sensor,ballistocardiography (BCG) sensor, an impedance plethysmography (IPG)sensor, a ultrasound sensor, a force sensor, and any combinationsthereof.

In some implementations, the at least one second biometric sensor isselected from the group consisting of: a photoplethysmorgraphy (PPG)sensor, an ECG sensor, a phonocardiography (PCG) sensor,ballistocardiography (BCG) sensor, an impedance plethysmography (IPG)sensor, a ultrasound sensor, a force sensor, and any combinationsthereof.

In some implementations, the wrist-worn device further includes: atemperature sensor, an accelerometer, a gyroscope, an altimeter, a GPSsensor, and any combinations thereof.

In some implementations, the one or more processors are configured to:obtain a pulse transit time or a value derived therefrom using dataobtained from the at least one first biometric sensor and the at leastone second biometric sensor; and apply the pulse transit time or thevalue derived therefrom to a model to obtain an estimate of bloodpressure, wherein the model takes the pulse transit time or the valuederived therefrom as an input and provides a blood pressure measurementas an output.

In some implementations, the system is configured to calibrate themodel.

In some implementations, the at least one first biometric sensorincludes an ECG sensor; the at least one second biometric sensorincludes a PPG sensor for capturing distal pulse waves; and the one ormore processors are configured to estimate blood pressure using dataobtained from the ECG sensor and the PPG sensor.

Another aspect of the disclosure relates to wearable device formeasuring blood pressure. The device includes: a calibration bloodpressure measuring device that does not rely on pulse transit time tomeasure blood pressure; one or more sensors configured to obtain pulsetransit time; a memory; and one or more processors communicativelylinked to the calibration blood pressure measuring device, the one ormore sensors, and the memory. The one or more processors are configuredto: (a) take at least one measurement of a blood pressure of a personwith the calibration blood pressure device when the person is performinga Valsalva maneuver or a cold pressor maneuver; (b) obtain proximalpulse wave data and distal pulse wave data from the one or more sensorswhen the person is performing the Valsalva maneuver or the cold pressormaneuver; (c) obtain at least one pulse transit time using the proximalpulse wave data and the distal pulse wave data obtained when the personis performing the Valsalva maneuver or the cold pressor maneuver; (d)obtain at least one calibration data point corresponding to the at leastone measurement of the blood pressure and the at least one pulse transittime; and (e) fit a model to two or more data points including the atleast one calibration data point, wherein the model relates the bloodpressure of the person to the pulse transit time of the person.

In some implementations, the one or more processors are furtherconfigured to: obtain a test pulse transit time from the user; and applythe test pulse transit time to the model to obtain an estimate of bloodpressure.

In some implementations, the wearable device further includes atemperature sensor, where in the one or more processors are furtherconfigured to initiate step (a) when they determine that temperaturedata from the temperature sensor indicates that a temperature reductionmeets a criterion.

In some implementations, the wearable device further includes a PPGsensor, where in the one or more processors are further configured toinitiate step (a) when they determine that the PPG data from thetemperature sensor indicates that a PPG signal reduction meets acriterion.

In some implementations, the one or more sensors include a firstultrasound sensor configured to capture the proximal pulse wave data anda second ultrasound sensor configured to capture the distal pulse wavedata. In some implementations, the first and second ultrasound sensorsare enclosed in a wrist worn device. In some implementations, the firstultrasound sensor is exposed on the wrist worn device and allows contactwith the user's chest when the user moves his or her wrist to touch hisor her chest when the wrist worn device is worn on the wrist, andwherein the second ultrasound sensor is disposed to contact a wrist whenthe wrist worn device is worn on the wrist.

In some implementations, the one or more sensors includes an ultrasoundsensor configured to capture the proximal pulse wave data and aphotoplethysmography (PPG) sensor configured to capture the distal pulsewave data.

In some implementations, the one or more sensors includes an ultrasoundtransceiver.

In some implementations, the wearable device includes a wrist-worndevice including a wrist band. In some implementations, the sensorconfigured to generate the distal pulse wave data is disposed to contacta wrist when the wrist worn device is worn on the wrist. In someimplementations, the sensor configured to generate the proximal pulsewave data is disposed to be exposed and allow contact with the user'schest when the user moves his or her wrist to touch his or her chest.

In some implementations, the one or more sensors includes aphotoplethysmorphgraphy (PPG) sensor configured to contact the user'swrist for generating distal pulse wave data and an electrocardiogram(ECG) sensor for capturing ECG data, and wherein the one or moreprocessors are configured to determine a pulse transit time using theECG data and the distal pulse wave data. In some implementations, theECG sensor includes a first electrode exposed on one surface of thehousing of the device and a second electrode exposed on a second surfaceof the housing. In some implementations, the first electrode isconfigured to contact the user's wrist when the device is worn on thewrist, and the second electrode is configured to be exposed and notcontacting the wrist when the device is worn on the wrist.

In some implementations, the wrist worn device further includes at leastone sensor selecting form the group consisting of: a force sensor, atemperature sensor, an accelerometer, a gyroscope, an altimeter, a GPSsensor, and any combinations thereof.

These and other objects and features of the present disclosure willbecome more fully apparent from the following description, withreference to the associated drawings and appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates a scheme of some implementations in which PPG dataare used to obtain a proximal pulse wave 101 and a distal pulse wave103.

FIG. 1B illustrates a scheme of some implementations in which ECGwaveform 105 in the top panel and distal PPG waveform 107 in the bottompanel are used to estimate a PTT.

FIG. 2A illustrates that multiple cycles of an EKG waveform and multiplecycles of a PPG waveform are used to derive multiple PTT estimates.

FIG. 2B shows waveforms that have not been smoothed on the left andwaveforms that have been smoothed, e.g., by techniques described above,on the right.

FIGS. 3A-3C illustrate various combinations of placement locations forPTT sensors according to some embodiments of the disclosure.

FIG. 4 shows a flow chart illustrating process 400 for calibrating andoperating a PTT-based blood pressure measurement system according toimplementations of the disclosure.

FIG. 5 illustrates an example of multiple calibration data points and arelationship of a model that describes BP as a non-linear function ofPTT.

FIG. 6 shows a flow chart illustrating process 600 for calibrating aPTT-based blood pressure measurement system involving a user holding anarm at different position according to implementations of thedisclosure.

FIG. 7 shows a flow chart illustrating process 700 for calibrating aPTT-based blood pressure measurement system involving applying externalpressures according to implementations of the disclosure.

FIG. 8A illustrates an example portable monitoring device which enablesuser interaction via a user interface.

FIG. 8B is a block diagram showing components of a biometric monitoringdevice according to some implementations of the disclosure.

FIG. 9A illustrates an example portable monitoring device which may besecured to the user through the use of a band.

FIG. 9B provides a view of the example portable monitoring device ofFIG. 9A which shows the skin-facing portion of the device.

FIG. 9C provides a cross-sectional view of the portable monitoringdevice of FIG. 9A.

FIG. 10A provides a cross sectional view of a sensor protrusion of anexample portable monitoring device.

FIG. 10B depicts a cross sectional view of a sensor protrusion of anexample portable monitoring device; this protrusion is similar to thatpresented in FIG. 10A with the exception that the light sources andphotodetector are placed on a flat and/or rigid PCB.

FIG. 10C provides another cross-sectional view of an example PPG sensorimplementation.

FIG. 11A illustrates an example of one potential PPG light source andphotodetector geometry.

FIGS. 11B and 11C illustrate examples of a PPG sensor having aphotodetector and two LED light sources.

FIG. 12 Illustrates an example of an optimized PPG detector that has aprotrusion with curved sides so as not to discomfort the user.

FIG. 13A illustrates an example of a portable monitoring device having aband; optical sensors and light emitters may be placed on the band.

FIG. 13B illustrates an example of a portable biometric monitoringdevice having a display and wristband. Additionally, optical PPG (e.g.,heart rate) detection sensors and/or emitters may be located on the sideof the biometric monitoring device. In one embodiment, these may belocated in side-mounted buttons.

FIG. 14 depicts a user pressing the side of a portable biometricmonitoring device to take a heart rate measurement from a side-mountedoptical heart rate detection sensor. The display of the biometricmonitoring device may show whether or not the heart rate has beendetected and/or display the user's heart rate.

FIG. 15 illustrates functionality of an example biometric monitoringdevice smart alarm feature.

FIG. 16 illustrates an example of a portable biometric monitoring devicethat changes how it detects a user's heart rate based on how muchmovement the biometric monitoring device is experiencing.

FIG. 17 illustrates an example of a portable biometric monitoring devicethat has a bicycle application on it that may display bicycle speedand/or pedaling cadence, among other metrics.

FIG. 18A illustrates an example block diagram of a PPG sensor which hasa light source, light detector, ADC, processor, DAC/GPIOs, and lightsource intensity and on/off control.

FIG. 18B illustrates an example block diagram of a PPG sensor that issimilar to that of FIG. 18A which additionally uses a sample-and-holdcircuit as well as analog signal conditioning.

FIG. 18C illustrates an example block diagram of a PPG sensor that issimilar to that of FIG. 18A which additionally uses a sample-and-holdcircuit.

FIG. 18D illustrates an example block diagram of a a PPG sensor havingmultiple switchable light sources and detectors, light sourceintensity/on and off control, and signal conditioning circuitry.

FIG. 18E illustrates an example block diagram of a PPG sensor which usessynchronous detection. To perform this type of PPG detection, it has ademodulator.

FIG. 18F illustrates an example block diagram of a PPG sensor which, inaddition to the features of the sensor illustrated in FIG. 18A, has adifferential amplifier.

FIG. 18G illustrates an example block diagram of a PPG sensor which hasthe features of the PPG sensors shown in FIGS. 18A-18F.

FIG. 19A illustrates an example of a portable biometric monitoringdevice having a heart rate or PPG sensor, motion sensor, display,vibromotor, and communication circuitry which is connected to aprocessor.

FIG. 19B illustrates an example of a portable biometric monitoringdevice having a heart rate or PPG sensor, motion sensor, display,vibromotor, location sensor, altitude sensor, skin conductance/wetsensor and communication circuitry which is connected to a processor.

FIG. 19C illustrates an example of a portable biometric monitoringdevice having physiological sensors, environmental sensors, and locationsensors connected to a processor.

DETAILED DESCRIPTION Introduction

The instant disclosure provides devices and methods for measuring bloodpressure using pulse transit time. In some implementations, a wearablebiometric monitoring device is provided with sensors configured toobtain different types of biometric data that can be used to determinepulse transit time. In some implementations, the wearable device isconfigured as a wrist worn device including all the sensors used toobtain data for determining pulse transit time. In some otherimplementations, the wearable device is configured as a wrist worndevice communicatively linked to another sensor device, where the wristworn device and the other sensor device collectively provide data fordetermining pulse transit time.

In various implementations of the disclosure, the device does notrequire an inflatable cuff to measure or estimate blood pressure afterthe device has been properly calibrated. The device, consistent withsuch an embodiment, can be less cumbersome and more wearable/mobile thanother (e.g., conventional) devices.

In some implementations, the device can provide continuous estimates ofblood pressure. In some implementations, the device can obtain anestimate of blood pressure faster than conventional methods such asoscillometric blood pressure measurement (OBPM) methods or auscultationmethods. The improved speed of measurement is in part related to theelimination of the need to slowly adjust the pressure of an inflatablecuff when estimating blood pressure using PTT.

Some implementations of the disclosure provide convenient and/or userfriendly means for blood pressure measurements. Therefore, the devicemay be used without a trained professional. Using devices of someimplementations of the disclosure, blood pressure may be obtained morefrequently than conventional methods and/or obtained under a widevariety of conditions, such as when the user is ambulatory or performingdaily activities, e.g., sleeping, resting, exercising, walking, ortaking weight measurements on a scale, etc.

Some implementations of the instant disclosure provide reliableBP-to-PTT calibrations using blood pressure perturbation methods andmultiple sensors. Such calibrations may improve the accuracy of bloodpressure measurements compared to conventional methods or devices.

In some implementations, the conditions for performing the calibrationsor the process of the calibrations may be automatically controlled bythe device. Such automation improves the accuracy of the calibrationsand user-friendliness of the device compared to conventional devices andcalibration methods.

Pulse Transit Time (PTT) and Blood Pressure

When the heart pumps blood into the aorta, the pumping action generatesa pressure wave (i.e., the pulse wave) that travels along the arteries.The speed of the pulse wave depends on the tension of the arterialwalls. When the blood pressure is high, the arterial walls are tense andhard and the pulse wave travels faster. When the blood pressure is low,the arterial walls have less tension and the pulse wave travels slower.Therefore, there is a negative correlation between pulse wave velocity(PWV) and blood pressure. Pulse transit time (PTT) is the time it takesa pulse pressure waveform or pulse wave to propagate through a length ofthe artery. PWV=d/PTT, wherein d is the length of the artery. For agiven length of the artery, there is a negative correlation between PTTand blood pressure. The instant disclosure exploits this correlation toestimate blood pressure.

This disclosure provides different ways to measure PTT. One way is tomeasure the arrival time of a proximal pulse wave that is closer to theheart (or proximal arrival time) and the arrival time of a distal pulsewave that is further away from the heart (or distal arrival time). Thedifference between the two arrival times provides the PTT. For instance,a proximal pulse wave may be represented by PPG data collected from thechest of the upper arm, and a distal pulse wave may be represented byPPG data collected at the wrist or the foot. The arrival time of thepulse wave may be measured by various physiological sensors or datagraphical representations. For instance, a pulse arrival time may beestimated from photoplethysmography (PPG), phonocardiography (PCG),ballistocardiography (BCG), ultrasound, or bioimpedance data. Also, asfurther explained below, proximal arrival time may be estimated orapproximated from electrical signal such as ECG and impedancecardiography (ICG).

In some implementations, the same type of data may be used to obtain theproximal arrival time and the distal arrival time. FIG. 1A illustrates ascheme of some implementations in which PPG data are used to obtain aproximal pulse wave 101 and a distal pulse wave 103, which may then beused to derive an estimate of PTT.

Various morphological features may be derived from a PPG waveform, whichcorresponds to a pulse wave. As illustrated here, the left peak 102 ofthe PPG waveform of the proximal wave 101 in the top panel and the leftpeak 104 of the PPG waveform of the distal wave 103 in the bottom panelare used to derive the PTT. The difference in times of the two peaksprovides an estimate of PTT.

Other morphological features of PPG, PCG, BCG or ultrasound graph may beused in some implementations for estimating PTT. For instance, asillustrated here for PPG data, the second peak, the inflection points,the left foot, ⅓, ½, or ⅔ from the left foot to the left peak, featuresof first and second derivative, and combinations thereof may be used inthe proximal wave and distal wave to estimate PTT.

Similarly, data other than PPG data, e.g., ultrasound data may be usedto obtain both the proximal arrival time and the distal arrival time.

In other implementations, different types of data may be used to measureproximal and distal pulse waves. For instance, one type of data may beused to obtain the proximal arrival time and another type of data may beused to obtain the distal arrival time, and a PTT can be estimated fromthe difference between the two arrival times. For example, ultrasounddata may be used to obtain the proximal arrival time and PPG data may beused to obtain the distal arrival time, or vice versa.

Another way for estimating PTT is to use the time difference betweenelectrical-signal-based data (e.g., ECG/ICG data) and the arrival timeof a distal wave, the latter may be measured by, e.g., PPG, ultrasound,PCT, or BCG. Because the propagation speed of ECG is negligible, thelocation from which the ECG data originate does not affect the timing ofECG morphology.

Proximal pulse wave data and distal pulse wave data may be data directlydescribing pressure of the pulse wave, or other data that correlate withpulse wave features. For instance, PPG or ultrasound data may directlyindicate the pressure of pulse waves. So PPG or ultrasound data mayprovide both proximal pulse wave data or distal pulse wave data.Moreover, the R wave peak of ECG data generally, though imperfectly,correlates with proximal pulse arrival time, so ECG data may be used asproximal pulse wave data in some implementations. ECG data reflectsproximal wave and may be used as proximal wave data. This is so even ifthe ECG data is obtained from a location distal from the heart becauseECG propagation time is negligible. ECG data may be combined with distaldata captured at a distal location, e.g., PPG, ultrasound, etc., toestimate PTT.

FIG. 1B illustrates a scheme of some implementations in which ECGwaveform 105 in the top panel and distal PPG waveform 107 in the bottompanel are used to estimate a PTT. In the example illustrated here, thepeak of the R wave 206 of the ECG data and the peak of the distal PPGwaveform 208 are used to derive the PTT, which corresponds to thedifference in time between the two peaks 206 and 208.

As mentioned above, different morphological features of the pulse waveforms may be used to estimate the PTT in various implementations. Forinstance, instead of the peaks of the two waveforms, the correspondingfeet of the two waveforms may be used to estimate the PTT. Moreover,different morphological features across the two waveforms may be used toestimate PTT in some implementations. In some implementations, when bothdistal and proximal waveforms are PPG waveforms, cross-correlationanalysis may be used to find the delay between the two waveforms.

In some implementations, raw sensor data are processed and analyzed toextract morphological features and estimate PTT. For example, in thecase of using ECG and PPG, in order to accurately measure the arrival ofa pulse, one or more data processing techniques are applied fordetecting various features of the pulse wave such as the foot, peak,maximum, 1^(st) derivative, or 2nd derivative. In some implementations,multiple heart beats can be averaged to generate a smooth template whichcan then be cross-correlated with the PPG signal to reliably measuremultiple PTT intervals. These intervals can then be combined togetherusing a computation that is robust to outliers such a median.

FIG. 2A illustrates that multiple cycles of an EKG waveform and multiplecycles of a PPG waveform are used to derive multiple PTT estimates. TheEKG waveform is shown in the top panel, the PPG waveform is shown in themiddle panel, and the PTT values derived from time difference betweenthe corresponding features in each cycle of the EKG and PPG waveformsare shown at the bottom panel. Multiple PTT values may be combined toobtain an estimate that is more resistant to noise, such as using themedian or mean of multiple values. In some implementations, thewaveforms may be processed by a smoothing algorithm, such as a rollingtime window smoothing technique or a low-pass filter that removeshigh-frequency oscillation. FIG. 2B shows waveforms that have not beensmoothed on the left and waveforms that have been smoothed, e.g., bytechniques described above, on the right. ECG data are shown in the tophalf and PPG data in the bottom half.

As mentioned above, the instant disclosure exploits the relation betweenPTT and blood pressure to estimate blood pressure. Under someconditions, the relation between PTT and blood pressure is roughlylinear. This relation may be deduced from models of the relation betweenPWV and blood pressure. For instance, the Moens-Korteweg equationprovides:

$\begin{matrix}{{PWV} = \sqrt{\frac{E_{inc}h}{2r\; \rho}}} & (1)\end{matrix}$

where E_(inc) is incremental elastic modulus, h is the thickness ofartery, r is the radius of blood vessel, and ρ is blood density. Namely,the Moens-Korteweg equation describes that PWV is proportional to thesquare root of the incremental elastic modulus of the vessel wall givenconstant ratio of wall thickness h to vessel radius r. Substituting thevariables PWV and E_(inc) as follows:

$\begin{matrix}{{PWV} = \frac{d}{PTT}} & (2) \\{E_{inc} = {E_{0}e^{aP}}} & (3)\end{matrix}$

where E₀ is elastic modulus of vessel was with no pressure, and a is theparameter that correlates to variation of elastic modulus E₀ dependingon blood pressure P, one gets:

$\begin{matrix}{\frac{d^{2}}{{PTT}^{2}} = \frac{E_{0}e^{aP}h}{2r\; \rho}} & (4)\end{matrix}$

After rearranging:

$\begin{matrix}{P = {{a\; \ln \frac{b}{{PTT}^{2}}} = {{a\left( {{\ln \frac{1}{{PTT}^{2}}} + {\ln \; b}} \right)} = {{a\; \ln \frac{1}{{PTT}^{2}}} + c}}}} & (5) \\{where} & \; \\{b = \frac{2{rpd}^{2}}{Eh}} & (6)\end{matrix}$

Therefore, blood pressure can be expressed as a function of PTT:

$\begin{matrix}{P = {{f({PTT})} = {{a\; \ln \frac{1}{{PTT}^{2}}} + c}}} & (7)\end{matrix}$

Although the mathematical relationship described by equation (7) is notlinear, it is can be roughly approximated by P=a*PTT+c for simpler andfaster calculation.

Moreover, because the measurements for PTT are affected by other factors(e.g., body posture) in addition to the variables described above, theactual blood pressure is not always precisely determined from therelationship above. However, the PTT to BP relationship or correlationis relatively robust and reliable. By modeling the relationship betweenPTT and BP, one can then use the model to predict BP from PTT,regardless of the precise nature of the model or the exact knowledge ofthe other factors.

Moreover, for the same reason, even if the PTT measurement does notreflect the exact difference between a distal arrival time and aproximal arrival time, so long as the error from the exact difference isconsistent across measures, the obtained PTT-BP relationship can stillbe used to estimate BP after being calibrated. If the PTT measurement isPTT_(actual)+error, and the error is consistent across measurements, thederived relationship may still effectively predict blood pressure, solong the error is consistent across measurements.

PTT Sensor Types and Placement

In various implementations, numerous technologies and related sensortypes are employed to measure proximal or distal pulse arrival time orPTT. The applicable technologies include but are not limited to:photoplethysmography (PPG), Electroardiography (EKG/ECG),phonocardiography (PCG), ballistocardiography (BCG), impedanceplethysmography or impedance phlebography (IPG), and ultrasound.

Phonocardiography (PCG) is diagnostic technique that creates a graphicrecord, or phonocardiogram, of the sounds and murmurs produced by thecontracting heart, including its valves and associated great vessels.The phonocardiogram can be obtained with a chest microphone or with aminiature sensor in the tip of a small tubular instrument that isintroduced via the blood vessels into one of the heart chambers. PCG canindicate pulse arrival time at a location, and therefore pulse transmittime between two locations.

Ballistocardiography (BCG) is a technique for producing a graphicalrepresentation of repetitive motions of the human body arising from thesudden ejection of blood into the great vessels with each heart beat.BCG may be obtained from motion data provided by motion sensors. Analternative example of obtaining a BCG is by use of aballistocardiographic scale, which measures the recoil of the person'sbody who is on the scale. BCG can indicate pulse arrival time at aspecific location, and therefore pulse transmit time between twolocations.

Impedance phlebography, or impedance plethysmography (IPG), is anon-invasive medical test that measures small changes in electricalresistance of the chest, calf or other regions of the body. Thesemeasurements reflect blood volume changes, and can indicate pulsearrival time, and therefore pulse transmit time between two locations.

As mentioned above, in some implementations, the same type of sensor ortechnology may be used to obtain PTT measurements or estimates. In otherimplementations, different sensors or technologies can be combined toacquire a PTT measurement or estimate.

In some implementations, a device includes a first ultrasound sensorconfigured to measure or estimate proximal pulse arrival time, and asecond ultrasound sensor configured to measure or estimate distal pulsearrival time. These two ultrasound units could be integrated into asingle device, e.g., a wrist worn device like the one shown in FIGS. 13Aand 13B, allowing simple and easy measurement of PTT by, for example,wearing the device on a wrist and holding it against the chest. In someimplementations the first ultrasound sensor is exposed on the wrist worndevice (e.g., like sensor 1312) and allows contact with the user's chestwhen the user moves his or her wrist to touch his or her chest when thewrist worn device is worn on the wrist. The first ultrasound sensor isconfigured to be exposed on a surface of the device, allowing contactwith the chest directly or through clothing, at a location such as 302shown in FIG. 3A. The second ultrasound sensor (e.g., like sensor 1310,1302, or 1303) is configured to contact the wrist of the user when wornon the wrist, allowing contact with the wrist at a location such as 306shown in FIG. 3A. In some implementations, the first or secondultrasound sensor includes an ultrasound transceiver. In someimplementations, the device includes an ultrasound sensor configured tocapture the proximal pulse wave data at, e.g., the chest 302 or theupper arm 304. It also includes a photoplethysmography (PPG) sensorconfigured to capture the distal pulse wave data at, e.g., the wrist 306or the lower limb 308.

In some implementations, the device is configured as a wrist-worn deviceand the wearable device further includes a wrist band. In someimplementations, the sensor configured to generate the distal pulse wavedata is disposed to contact a wrist when the wrist worn device is wornon the wrist.

FIG. 3A illustrates some combinations of placement locations for PTTsensors in some implementations. They are provided as examples and arenot limiting. Both proximal wave data and distal wave data need to becollected. The difference between the two waves is relative, so byincreasing the relative distance of two sensors as measured from theheart, the PTT is increased. It is therefore practical to place theproximal wave sensor close to the heart, and the distal wave sensor farfrom the heart. However, in some applications, a smaller differencebetween the arrival times of the two waves may still be used to estimateblood pressure. Also, when ECG sensor is used, it may be used toestimate proximal wave regardless of its location.

In the example shown in FIG. 3A, proximal pulse wave data may beobtained from the chest 302 or the upper arm 304, as indicated by “P” inthe figure. In various implementations, proximal pulse wave data may beobtained using, e.g., a PPG sensor, an ECG sensor, a PCG sensor, a BCGsensor, an ultrasound sensor, an IPG sensor, or a force sensor, ormultiple sensors of the kind same kind. For instance, distal pulse wavedata may be obtained from the wrist 306 or the foot or ankle 308, asindicated by “D” in the figure. In various implementations, distal pulsewave data may be obtained using, e.g., a PPG sensor, a PCG sensor, a BCGsensor, an ultrasound sensor, an IPG sensor, a force sensor, or multiplesensors of the same kind.

In some implementations, the biometric monitoring device includessensors for measuring PTT and other physiological sensors. The featuresand functions of other physiological sensors are further describedhereinafter. For instance, in addition to PTT sensors, the device insome implementations includes one or more of the following sensors: aforce sensor, a temperature sensor, an accelerometer, a gyroscope, analtimeter, and a GPS sensor.

In some implementations, as shown in FIGS. 13A and 13B, a wrist-worndevice 1301 includes one or more PPG sensors 1302 and 1303 configured tocontact the wrist when worn on the wrist, e.g., at location 314 shown inFIG. 3B. The PPG sensors are used to obtain distal pulse wave data. Thewrist-worn device 1301 includes an internal ECG electrode 13010 that isconfigured to contact the wrist when worn on the wrist, e.g., atlocation 316 shown in FIG. 3B. The wrist-worn device 1301 also includesan external electrode 1312 configured to allow a finger from theopposite hand to touch it and close the electrical path. The ECGelectrodes 13010 and 13012 are used to obtain EEG data, which can beused to approximate proximal pulse wave arrival time. Using the proximalwave data obtained using the ECG electrodes and the distal wave datafrom obtained using the PPG sensors, PTT values may be estimated.

FIG. 3B shows different sensor placements involving implementationswhere an ECG sensor is used to obtain proximal pulse wave data. Asmentioned above, because ECG signal propagation quickly, the ECGwaveform may be used to estimate proximal pulse wave from differentlocations of the body, e.g., the chest 312, the wrist 316, or the lowerextremities 319 (e.g., the foot or ankle). Meanwhile, distal pulse wavedata can be obtained from but location distal from the heart such as thewrist 314 or the foot or ankle 319.

In accord with the sensor placements of FIG. 3B, some implementationsprovide a device including a photoplethysmography (PPG) sensorconfigured to contact the user's wrist for generating distal pulse wavedata and. It also includes an electrocardiogram (ECG) sensor forcapturing ECG data. One or more processors of the device are configuredto determine a pulse transit time using the ECG data and the distalpulse wave data. The ECG sensor includes a first electrode exposed onone surface of the housing of the device and a second electrode exposedon a second surface of the housing. The first electrode is configured tocontact the user's wrist when the device is worn on the wrist, and thesecond electrode is configured to be exposed and not contacting thewrist when the device is worn on the wrist. See FIGS. 13A and 13Bdescribed above.

Different sensors such accelerometers, pressure sensors, or gyroscopes(measuring ballistocardiography or BCG) can be used to acquire theorigin of the pressure wave. The pressure sensors may include a forcesensor, force sensitive resistor, mechanical sensor, load sensor, loadcell, strain gauge, piezo sensor, membrane potentiometer, or any othersuitable pressure sensors. Furthermore, these sensors can also beincorporated in a weight scale and both devices can be used together toacquire a PTT measurement. See FIG. 3C.

Some implementations provide a system for measuring blood pressure. Thesystem includes a weight scale including at least one first biometricsensor and a wrist-worn device including at least one second biometricsensor. The weight scale and the wrist-worn device are communicativelylinked. The weight scale or the wrist worn device includes one or moreprocessors configured to estimate blood pressure using data obtainedfrom the at least one first biometric sensor and the at least one secondbiometric sensor.

FIG. 3C illustrates sensor placements of a system. The system includes aweight scale 336 comprising at least one first biometric sensor. In theshown implementation, the first biometric sensor includes an ECG sensor334. The ECG sensor is configured to obtain proximal pulse wave data.The system also includes a wrist-worn device including at least onesecond biometric sensor, which is configured to contact the user's wrist332. In some implementations, the at least one second biometric sensorincludes PPG sensor. The wrist-worn device may be implemented as shownin FIGS. 9A-9C.

In some alternative implementations, the sensor placements of FIG. 3Cmay be reversed, such that the weight scale includes a PPG sensor fordistal wave data and the wrist device includes an ECG sensor forproximal wave data. In either placement configuration, the ECG sensorobtains proximal pulse wave data and the other sensor obtains distalpulse wave data.

The at least one first or second biometric sensor is selected from: aphotoplethysmography (PPG) sensor, an ECG sensor, a phonocardiography(PCG) sensor, ballistocardiography (BCG) sensor, an impedanceplethysmography (IPG) sensor, a ultrasound sensor, a force sensor.

In some alternative implementations, the wrist-worn device furtherincludes other physiological sensors described herein, such as atemperature sensor, an accelerometer, a gyroscope, an altimeter, or aGPS sensor, etc.

In some alternative implementations, the one or more processors areconfigured to obtain a pulse transit time or a value derived therefromusing data obtained from the at least one first biometric sensor and theat least one second biometric sensor; and apply the pulse transit timeor the value derived therefrom to a model to obtain an estimate of bloodpressure, wherein the model takes the pulse transit time or the valuederived therefrom as an input and provides a blood pressure measurementas an output.

In some implementations, the system is configured to calibrate the modelusing one or more of the calibration methods described hereinafter.

Another embodiment would be to use an inflatable-bladder arm cuff incombination with the above PTT measurement techniques for measuringproximal (pulse origination) time. The arm cuff could be used to gathera pressure measurement for estimating the distal pulse arrival time.

Calibration of Blood Pressure Model

In some implementations, methods for calibrating a blood pressuremonitoring device are provided. In some implementations, systems forestimating blood pressure using pulse transit time are provided, wherethe systems apply PTT-blood pressure models that can be calibrated bythe user. Reliable BP-to-PTT calibration can be achieved using BPperturbation methods and multiple sensors.

FIG. 4 shows a flow chart illustrating process 400 for calibrating andoperating a PTT-based blood pressure measurement system according toimplementations of the disclosure. The system includes a calibrationblood pressure measuring device that does not use PTT to determine bloodpressure. The calibration blood pressure measuring device is used tocalibrate the system on an as-needed basis. After calibration, thesystem uses PTT to estimate blood pressure. The calibration bloodpressure measuring device may be implemented as a oscillometric bloodpressure device, a sphygmomanometer, a tonometry device, or a volumeclamp device. The system also includes one or more sensors configured tomeasure pulse transit time, a memory, and one or more processors.

The process 400 starts by determining whether conditions for calibratingthe system are met. Block 402. If the conditions are met, process 400proceeds to take a calibration blood pressure measurement. Block 404.

In some implementations, the system automatically determines whether theconditions for calibration are met. Block 402. In some implementations,the process automatically determines the conditions by obtaining PPGdata from the person during a time period when a Valsalva maneuver isperformed.

When a person forcefully expires against a closed air way, increasedintrathoracic pressure dramatically affect venous return, cardiacoutput, arterial pressure, and heart rate. This forced expiratory effortis called a Valsalva maneuver. During the initial phase of the Valsavamaneuver, compression of the thoracic aorta transiently increases bloodpressure (phase I). However, blood pressure begins to fall (phase II)after a few seconds because cardiac output falls. Changes in heart rateare reciprocal to the changes in aortic pressure due to the baroreceptorreflex. Therefore, during phase I of the Valsava maneuver, heart ratedecreases because blood pressure is elevated; during phase II, heartrate increases as blood pressure falls. Under certain conditions, aValsalva maneuver can lower systolic BP by about 20-30 mmHg for a periodof time of at least 10 seconds.

In some implementations, based on determining that an amplitude of thePPG data has decreased in the time period by more than a thresholdvalue, the system automatically proceed to take a calibration bloodpressure measurement using the calibration blood pressure measuringdevice. In some implementations, the process automatically determinesthe conditions by measuring heart rate of the person (e.g., by PPG)during a time period when the Valsalva maneuver is performed. Based ondetermining that the heart rate has increased in the time period by morethan a threshold value, the system automatically proceed to take acalibration blood pressure measurement using the calibration bloodpressure measuring device. In some implementations, the system utilizesinertial sensors to determine that the user has been stationary duringthe calibration process and that changes in PPG amplitude are not due tohand motions and changing hydrostatic pressure. When these conditionsare met, the process automatically proceeds to take a calibration bloodpressure measurement.

In some implementations involving performing a cold pressor maneuver forcalibrating the system, the system includes a temperature sensor formeasuring temperature at a position of a person in a time period whenthe person is performing a cold pressor maneuver. Based on determiningthat the temperature at the position meets a criterion, the systemautomatically triggers measurement of the calibration blood pressure.

In some implementations, the calibration blood pressure measurement istaken using the calibration blood pressure device when the person isperforming a Valsalva maneuver or a cold pressor maneuver. Block 404. Insome implementations, the user is instructed from the UI of the deviceto perform the maneuver at a specific point in time while at the sametime the system has initiated a cuff-based blood pressure measurementwith the right timing.

In some implementations, the user is instructed from the UI of thedevice to initiate a calibration of the system. In some implementations,the instructions are provided based on a time schedule. In someimplementations, instructions are provided based on a user context. Theuser context may indicates the following: the user was at a restaurant,the user recently consumed food, time is approaching the time that theuser typically goes to sleep, the user just exercised, the user justwoke up, the user just ended a commute, significant change has occurredin perfusion properties when stationary, or the hand to which one of thesensors is attached is at approximately the same orientation as before.

In some implementations, the system can use information about a user'slocation to determine that a user has recently visited a restaurant. Insome implementations, the user's location information is obtained from alocation sensor built into the system. In some implementations, theuser's location information is generated by another device (e.g., asmart phone or another biometric monitoring device). The locationinformation is used to determine, on the system or on another device(e.g., a server computer, a smart phone, or another biometric monitoringdevice), that the user has visited a restaurant.

In some implementations, determining the conditions for calibration thesystem 402 involves determining whether the current time is in acalibration time period in a defined schedule. For instance, the definedcalibration time may be a specific time of day, a specific day of theweek, a specific time from a defined time, or a specific time since thelast calibration session, etc. This allows the biometric monitoringdevice to calibrate based on a schedule or time considerations. Theschedule may be based on the user's history (e.g., typical times of dayor the week when the user is not engaged in strenuous activity), aprocessor's available bandwidth for performing PWA, and the like. If thecurrent time is not within a defined calibration time period, the devicedoes not trigger blood pressure data collection and wait for the nexttime interval to examine the condition again.

In some implementations, determining the conditions for calibration thesystem 402 involves determining whether a user request for calibratingthe system is received. In some implementations, if the user request forcalibrating the system is received (e.g., through an on-device menu,input device (physical or virtual button), or a connected device (e.g.,a mobile phone)), the system provides instructions to the person thatinstruct the person to initiate the calibration based on the userrequest. In some implementations, the instructions include aninstruction to perform a Valsalva maneuver or a cold pressor maneuver.In some implementations, if the user request for calibrating the systemis received, the system initiates sensors, based on the user request, toobtain proximal pulse wave data and distal pulse wave data. Process 400further involves obtaining a calibration pulse transit time. See block406. In some implementations, this involves obtaining proximal pulsewave data and distal pulse wave data from the one or more sensors whenthe person is performing the Valsalva maneuver, and obtaining at leastone pulse transit time using the proximal pulse wave data and the distalpulse wave data obtained when the person is performing the Valsalvamaneuver. In other implementations, this involves obtaining proximalpulse wave data and distal pulse wave data from the one or more sensorswhen the person is performing the cold pressor maneuver, and obtainingat least one pulse transit time using the proximal pulse wave data andthe distal pulse wave data obtained when the person is performing thecold pressor maneuver.

Process 400 then obtain at least one calibration data pointcorresponding to the calibration blood pressure measurement and the atleast one pulse transit time. See block 408.

In some implementations, the process decides whether in the calibrationdata points have been collected. See block 410. If more calibration datapoints are necessary, the process loops back to block 402. The previousoperations may be repeated one or more times to obtain one or moreadditional calibration data points. When enough calibration data pointshave been collected, the process proceeds to fit the model to thecalibration data points. See block 412. The model relates blood pressureto PTT. In some implementations, the model includes a linearmathematical relationship, a general linear model, a non-linear model,or a neural network model. In some implementations, the non-linear modeldescribes blood pressure as a nonlinear function of PTT as shown inequation (7). In other implementations, the linear model describes bloodpressure as a decreasing linear function of PTT. In someimplementations, fitting the model to the calibration data pointsinvolves obtaining estimates of model parameters to minimize thedistance between the calibration data points and the model prediction.In some implementations, fitting the model to the calibration datapoints involves obtaining estimates of model parameters to maximize thelikelihood of the model. FIG. 5 illustrates an example of multiplecalibration data points and a relationship of a model that describes BPas a non-linear function of PTT, such as that of equation (7). Inalternative implementations, a linear model may be fit to the samecalibration data points showing the figure.

In some implementations, the two or more data points to which the modelis fitted includes a baseline data point. The baseline data pointcorresponds to: a baseline measurement of a blood pressure of the personobtained when the person is in a baseline state, and a baseline pulsetransit time obtained when the person is in the baseline state. Thebaseline state can be a state when the user is not engaged in thecalibration maneuver such as a Valsalva maneuver or a cold pressormaneuver. For instance, the baseline state can be when the user isresting.

In some implementations, the system can be used to obtain test bloodpressures based on PTT using the PTT-BP model obtained in 412. To obtainthe PTT-based blood pressure measurement, the process involves obtainingproximal pulse wave data and distal pulse wave data using the one ormore PTT sensors, obtaining a test PTT estimate from the proximal pulsewave data and the distal pulse wave data. See block 414. Process 412then applies the test pulse transit time to the model to obtain anestimate of blood pressure. See block 416. As explained above, thePTT-based blood pressure measurement can be obtained more quickly andconveniently than conventional blood pressure measurements.

In some implementations, the system has the capability to determine thecondition to recalibrate the PTT-BP model. See block 418. For instance,it can determine whether a set schedule for recalibration is due. Forinstance, recalibration may be due every n hours, every day, every week,every month, etc. If so, process 400 restarts the calibration process bylooping back to block 402. In some implementations, the conditions forrecalibration are base are based on a user context. The user context mayindicates the following: the user was at a restaurant, time isapproaching the time that the user typically goes to sleep, the userjust exercised, the user just woke up, the user just ended a commute,significant change has occurred in perfusion properties when stationary,or the hand to which one of the sensors is attached is at approximatelythe same orientation as before. If no recalibration is required, theprocess ends at block 420.

FIG. 6 shows a flow chart illustrating process 600 for calibrating aPTT-based blood pressure measurement system involving a user holding anarm at different position according to implementations of thedisclosure. Process 600 is implemented on a system including analtimeter, a calibration blood pressure device for measuring bloodpressure, one or more sensors for measuring pulse transit time, amemory, and one or more processors. Similar to process 400, thecalibration blood pressure device does not rely on pulse transit time tomeasure blood pressure.

Process 600 starts by obtaining a measurement of blood pressure of theperson using the blood pressure device when the person is holding a limbat a position with the system attached to the limb. See block 604.

Process 600 also involves obtaining a set of altitude data from thealtimeter when the person is holding a limb at a position with thesystem attached to the limb. See block 606. Process 600 then obtains anestimate of hydrostatic pressure from the set of altitude data. Seeblock 608. In some implementations, the system further includes aninertial sensor, and process 600 further involves obtaining a set ofinertial data from the inertial sensor when the person is holding thelimb at the position. And the estimate of hydrostatic pressure isobtained from the set of inertial data and the set of altitude data. Insome implementations, the inertial sensor includes an accelerometer, agyroscope, or a magnetometer. In some implementations, the altimeter,the inertial sensor, and the calibration blood pressure device areattached to a same area of the limb when the measurement of bloodpressure is obtained.

Process 600 also involves obtaining proximal pulse wave data and distalpulse wave data by using the one or more sensors when the person isholding the limb at the first position. See block 610. Then it obtains apulse transit time from the proximal pulse wave data and the distalpulse wave data. See block 612.

Process 600 then loops back to block 604 and repeats blocks 604 to 612one or more times when the person is holding the limb at one or moredifferent positions, thereby obtaining two or more calibration datapoints corresponding to two or more measurements of blood pressure, twoor more pulse transit times, and two or more measurements of hydrostaticpressure. See blocks 614. Process 600 then fits a model to data pointsincluding the two or more calibration data points, where the modelrelates blood pressure to pulse transit time and hydrostatic pressure.See block 616.

The following equation describes the relation of components oftransmural blood pressure:

P _(tm) =P _(i) −P _(e) +P _(p)  (8)

where P_(tm) is transmural pressure, P_(i) is internal pressure, P_(e)is external pressure, and P_(p) is perturbation pressure includinghydrostatic pressure. The P in equation (7) is equivalent to Ptm inequation (8). By plugging equation (8) into equation (7), nonlinear arelationship may be obtained, which may be used to build a model inblock 616.

FIG. 7 shows a flow chart illustrating process 700 for calibrating aPTT-based blood pressure measurement system involving applying externalpressures according to implementations of the disclosure. Process 700 isimplemented on a system including an inflatable cuff, a calibrationblood pressure device for measuring blood pressure, one or more sensorsfor measuring pulse transit time, a memory, and one or more processors.Similar to process 400, the calibration blood pressure device does notrely on pulse transit time to measure blood pressure. In someimplementations, the inflatable cuff includes a pressure sensor.

Process 700 starts by applying an external pressure to a person usingthe inflatable cuff. See block 704. In some implementations, the systemdetermines the external pressure using the pressure sensor in theinflatable cuff. Process 700 also involves obtaining a measurement ofinternal blood pressure of a person with the calibration blood pressuredevice when the external pressure is applied to the person. See block706.

Process 700 then obtains a transmural blood pressure level from themeasurement of internal blood pressure and the external pressure. Seeblock 708. In some implementations, the transmural BP may be obtainedaccording to equation (8). In some implementations, the process includesintegrating an effect of the external pressure based on a length of thearm segment that is compressed by the inflatable cuff. In someimplementations, the method includes normalizing the integrated effectby a total length that a pulse wave travels. In some implementations,the total length that the pulse wave travels is estimated from proximalpulse wave data and distal pulse wave data. In some implementations, thetotal length that the pulse wave travels is estimated from inertial dataand/or altitude data. In some implementations, the total length that thepulse wave travels is provided by user input or pre-program estimates.

Process 700 also obtains proximal pulse wave data and distal pulse wavedata from the one or more sensors (block 710), and estimates a pulsetransit time using the proximal pulse wave data and the distal pulsewave data (block 712).

Process 700 then loops back to block 704 and repeats blocks 604 to 712one or more times when one or more different external pressures areapplied, thereby obtaining two or more calibration data pointscorresponding to two or more measurements of transmural blood pressure,and two or more pulse transit times. See blocks 714. Process 700 thenfits a model to data points including the two or more calibration datapoints, where the model relates transmural blood pressure to pulsetransit time. See block 716.

The automatic recalibration mechanisms described for process 400 arealso applicable to processes 600 and 700.

The above mentioned morphological features are relevant for arterialstiffness measurements. Examples of other useful morphological featuresinclude but are not limited to: ejection duration, heart rate, pressureat first shoulder, pressure at second shoulder, pressure at end systole,augmented pressure, mean diastolic pressure, mean arterial pressure,mean systolic pressure, tension time index, diastolic time index,subendocardial variability ratio, maximum rate of rise, reference age,acceleration features (e.g., those determined from the derivatives), andstiffness index. In some implementations, voltage or volume signals,instead of pressure signals are measured or analyzed. The voltage orvolume signals may be normalized. In these implementations, voltagevalues may be mapped to volume values, which may be non-linaerly mappedto pressure values. As such, the morphological features of pressuredescribed above may be indicated by voltage or volume.

Device Configuration

This disclosure is directed at biometric monitoring devices (which mayalso be referred to herein and in any references incorporated byreference as “biometric tracking devices,” “personal health monitoringdevices,” “portable monitoring devices,” “portable biometric monitoringdevices,” “biometric monitoring devices,” or the like), which may begenerally described as wearable devices, typically of a small size, thatare designed to be worn relatively continuously by a person. Variousimplementations of the disclosure relate to biometric monitoring devicethat can estimate blood pressure using PTT. Biometric monitoring devicesaccording to various implementations include sensors for generatingbiometric data for estimating PTT. In some implementations, thebiometric data may be used as proximal pulse wave data or distal pulsewave data. Various sensors that may be used to generate data for PTTestimation are described herein.

When worn, disclosed biometric monitoring devices gather data regardingactivities performed by the wearer or the wearer's physiological state.Such data may include data representative of the ambient environmentaround the wearer or the wearer's interaction with the environment,e.g., motion data regarding the wearer's movements, ambient light,ambient noise, air quality, etc., as well as physiological data obtainedby measuring various physiological characteristics of the wearer, e.g.,heart rate, perspiration levels, etc.

Biometric monitoring devices, as mentioned above, are typically small insize so as to be unobtrusive for the wearer. Fitbit offers severalvarieties of biometric monitoring devices that are all quite small andvery light, e.g., the Fitbit Flex™ is a wristband with an insertablebiometric monitoring device that is about 0.5″ wide by 1.3″ long by0.25″ thick. Biometric monitoring devices are typically designed to beable to be worn without discomfort for long periods of time and to notinterfere with normal daily activity.

In some cases, a biometric monitoring device may leverage other devicesexternal to the biometric monitoring device, e.g., an external pulsewaveform monitor or heart rate monitor in the form of an EKG sensor on achest strap may be used to obtain pulse waveform data or a GPS receiverin a smartphone may be used to obtain position data. In such cases, thebiometric monitoring device may communicate with these external devicesusing wired or wireless communications connections. The conceptsdisclosed and discussed herein may be applied to both stand-alonebiometric monitoring devices as well as biometric monitoring devicesthat leverage sensors or functionality provided in external devices,e.g., external sensors, sensors or functionality provided bysmartphones, etc.

In general, the concepts discussed herein may be implemented instand-alone biometric monitoring devices as well as, when appropriate,biometric monitoring devices that leverage external devices.

It is to be understood that while the concepts and discussion includedherein are presented in the context of biometric monitoring devices,these concepts may also be applied in other contexts as well if theappropriate hardware is available. For example, many modern smartphonesinclude motion sensors, such as accelerometers, that are normallyincluded in biometric monitoring devices, and the concepts discussedherein may, if appropriate hardware is available in a device, beimplemented in that device. In effect, this may be viewed as turning thesmartphone into some form of biometric monitoring device (although onethat is larger than a typical biometric monitoring device and that maynot be worn in the same manner). Such implementations are also to beunderstood to be within the scope of this disclosure.

The functionality discussed herein may be provided using a number ofdifferent approaches. For example, in some implementations a processormay be controlled by computer-executable instructions stored in memoryso as to provide functionality such as is described herein. In otherimplementations, such functionality may be provided in the form of anelectrical circuit. In yet other implementations, such functionality maybe provided by a processor or processors controlled bycomputer-executable instructions stored in a memory coupled with one ormore specially-designed electrical circuits. Various examples ofhardware that may be used to implement the concepts outlined hereininclude, but are not limited to, application specific integratedcircuits (ASICs), field-programmable gate arrays (FPGAs), andgeneral-purpose microprocessors coupled with memory that storesexecutable instructions for controlling the general-purposemicroprocessors.

Standalone biometric monitoring devices may be provided in a number ofform factors and may be designed to be worn in a variety of ways. Insome implementations, a biometric monitoring device may be designed tobe insertable into a wearable case or into multiple, different wearablecases, e.g., a wristband case, a belt-clip case, a pendant case, a caseconfigured to be attached to a piece of exercise equipment such as abicycle, etc. Such implementations are described in more detail in, forexample, U.S. patent application Ser. No. 14/029,764, filed Sep. 17,2013, which is hereby incorporated by reference for such purpose. Inother implementations, a biometric monitoring device may be designed tobe worn in only one manner, e.g., a biometric monitoring device that isintegrated into a wristband in a non-removable manner may be intended tobe worn only on a person's wrist (or perhaps ankle).

Portable biometric monitoring devices according to embodiments andimplementations described herein may have shapes and sizes adapted forcoupling to (e.g., secured to, worn, borne by, etc.) the body orclothing of a user. An example of a wearable biometric monitoring deviceis shown in FIG. 8; the example portable monitoring device may have auser interface, processor, biometric sensor(s), memory, environmentalsensor(s) and/or a wireless transceiver which may communicate with aclient and/or server.

In some implementations, the wearable biometric monitoring deviceintegrates a plurality of biometric sensors. FIG. 8B is a block diagramshowing components of a biometric monitoring device in such animplementation. The biometric monitoring device includes a processor, adisplay element, communication circuitry, and a plurality of biometricsensors that are communicatively linked and contained in a housingstructure. In some implementations, instead of enclosed in a housing,one or more of the sensors may be integrated into auxiliary structureconnected to the housing structure. For example, the PPG sensor may beintegrated into a wristband that is attached to a housing, providing adevice with a form similar to a wrist-watch that can be worn on thewrist. See FIG. 13A. The plurality of the biometric sensors in theexample illustrated in FIG. 8B includes a PPG sensor, an ECG sensor, aninertial or motion sensor, a temperature sensor, and an altimeter.

In some implementations, the PPG sensor is configured to collect datafor deriving pulse waveforms under operational conditions as describedabove in association with pulse wave analysis. In some implementations,the PPG sensor can have additional operation modes for collecting datafor other biometric measurements such as heartbeat, skin proximity, andskin color.

The wearable biometric monitoring device illustrated here also includesan inertial sensor or a motion sensor that can be used to detect motionor the lack of motion of a user wearing the device. In someimplementations, the device can use the motion information to removemotion noise from PDG data used for PWA. In some implementations, thedevice can use the motion information to determine activities of theuser, which activity information can be used in PWA. The inertial sensorcan also be used, either alone or in combination with other sensors, theposition or orientation of the user's body part to which the device isattached (e.g., the orientation or position of the user's wrist or arm).In some implementations, such information may be used to calibrate thePPG and/or taken into account in PWA.

The wearable biometric monitoring device also includes a temperaturesensor that can be used to measure the user's skin temperature, whichcan be used to normalize PPG data or accounted for in PWA.

The wearable biometric monitoring device also includes a pressure sensorthat can be used to measure the pressure between the sensor and theuser's tissue, which can affect the features and dynamics of the pulsewave. In some implementations, the measured the pressure can be used tonormalize PPG data or accounted for in PWA. The pressure sensor mayinclude one or more of the following or combinations thereof: a forcesensor, a force sensitive resistor, a mechanical sensor, a load sensor,a load cell, a strain gauge, a piezo sensor, or a membranepotentiometer.

The wearable biometric monitoring device also includes an optionallocation sensor and an optional altimeter. These sensors are optional insome implementations so they all illustrated by dashed lines.

An example of a wrist-worn portable biometric monitoring device is shownin FIGS. 9A through 9C. This device may have a display, button(s),electronics package, and/or an attachment band. The attachment band maybe secured to the user through the use of hooks and loops (e.g.,Velcro), a clasp, and/or a band having memory of its shape, e.g.,through the use of a spring metal band. In FIG. 9B, a sensor protrusionand recess for mating a charger and/or data transmission cable can beseen. In FIG. 9C, a cross-section through the electronics package isshown. Of note are the sensor protrusion, main PCB board, and display.

Portable biometric monitoring devices may collect one or more types ofphysiological and/or environmental data from embedded sensors and/orexternal devices and communicate or relay such information to otherdevices, including devices capable of serving as an Internet-accessibledata sources, thus permitting the collected data to be viewed, forexample, using a web browser or network-based application. For example,while the user is wearing a biometric monitoring device, the biometricmonitoring device may calculate and store the user's step count usingone or more biometric sensors. The biometric monitoring device may thentransmit data representative of the user's step count to an account on aweb service (e.g., www.fitbit.com), computer, mobile phone, or healthstation where the data may be stored, processed, and visualized by theuser. Indeed, the biometric monitoring device may measure or calculate aplurality of other physiological metrics in addition to, or in place of,the user's step count. These include, but are not limited to, energyexpenditure, e.g., calorie burn, floors climbed and/or descended, heartrate, heart rate variability, heart rate recovery, location and/orheading, e.g., through GPS, GLONASS, or a similar system, elevation,ambulatory speed and/or distance traveled, swimming lap count, swimmingstroke type and count detected, bicycle distance and/or speed, bloodpressure, blood glucose, skin conduction, skin and/or body temperature,muscle state measured via electromyography, brain activity as measuredby electroencephalography, weight, body fat, caloric intake, nutritionalintake from food, medication intake, sleep periods, e.g., clock time,sleep phases, sleep quality and/or duration, pH levels, hydrationlevels, respiration rate, and other physiological metrics. The biometricmonitoring device may also measure or calculate metrics related to theenvironment around the user such as barometric pressure, weatherconditions (e.g., temperature, humidity, pollen count, air quality,rain/snow conditions, wind speed), light exposure (e.g., ambient light,UV light exposure, time and/or duration spent in darkness), noiseexposure, radiation exposure, and magnetic field. Furthermore, thebiometric monitoring device or the system collating the data streamsfrom the biometric monitoring device may calculate metrics derived fromsuch data. For example, the device or system may calculate the user'sstress and/or relaxation levels through a combination of heart ratevariability, skin conduction, noise pollution, and sleep quality. Inanother example, the device or system may determine the efficacy of amedical intervention, e.g., medication, through the combination ofmedication intake, sleep data, and/or activity data. In yet anotherexample, the biometric monitoring device or system may determine theefficacy of an allergy medication through the combination of pollendata, medication intake, sleep and/or activity data. These examples areprovided for illustration only and are not intended to be limiting orexhaustive. Further embodiments and implementations of sensor devicesmay be found in U.S. Pat. No. 9,042,971, titled “Biometric MonitoringDevice with Heart Rate Measurement Activated by a Single User Gesture”and filed on Jan. 13, 2014 (Attorney Docket No. FTBTP008AUS), U.S. Pat.No. 9,044,149, titled “Pulse waveform data Collection” and filed on May29, 2014 (Attorney Docket No. FTBTP002X1AUS), U.S. Pat. No. 8,948,832,titled “Wearable Pulse waveform monitor or heart rate monitor” and filedon May 30, 2014 (Attorney Docket No. FTBTP002X1GUS), U.S. patentapplication Ser. No. 13/156,304, titled “Portable Biometric MonitoringDevices and Methods of Operating Same” filed on Jun. 8, 2011 and U.S.Patent Application 61/680,230, titled “Fitbit Tracker” filed Aug. 6,2012, which are hereby incorporated herein by reference in theirentireties.

Physiological Sensors

Biometric monitoring devices as discussed herein may use one, some orall of the following sensors to acquire physiological data, including,but not limited to, the physiological data outlined in the table below.All combinations and permutations of physiological sensors and/orphysiological data are intended to fall within the scope of thisdisclosure. Biometric monitoring devices may include but are not limitedto types of one, some, or all of the sensors specified below for theacquisition of corresponding physiological data; indeed, other type(s)of sensors may also or alternatively be employed to acquire thecorresponding physiological data, and such other types of sensors arealso intended to fall within the scope of the present disclosure.Additionally, the biometric monitoring device may derive thephysiological data from the corresponding sensor output data, but is notlimited to the number or types of physiological data that it couldderive from said sensor.

Physiological Sensors Physiological data acquired Optical ReflectometerHeart Rate, Heart Rate Variability Example Sensors: SpO₂ (Saturation ofPeripheral Oxygen) Light emitter and receiver, PPG sensors RespirationMulti or single LED and photo diode Stress arrangement Blood pressureWavelength tuned for specific physiological Arterial Stiffness signalsBlood glucose levels Synchronous detection/amplitude modulation Bloodvolume Heart rate recovery Cardiac health PPG Motion Detector Activitylevel detection Example Sensors: Sitting/standing detection Inertialsensors, Gyroscopic sensors, and/or Fall detection AccelerometersBallistocardiography (pulse waveform, pulse GPS arrival time, pulsetransmit time, pulse wave velocity) Skin Temperature Stress EMG(eletromyographic sensor) Muscle tension EKG or ECG(electrocardiographic sensor) Heart Rate Example Sensors: Heart RateVariability Single-lead ECG or EKG Heart Rate Recovery Dual-lead ECG orEKG Stress Cardiac health Pulse waveform, pulse arrival time, pulsetransmit time, pulse wave velocity Magnetometer Activity level based onrotation Laser Doppler Power Meter Ultrasonic Sensor Blood flow Pulsewaveform, pulse arrival time, pulse transmit time, pulse wave velocityAudio Sensor Heart Rate Heart Rate Variability Heart Rate Recovery Laughdetection Respiration Respiration type, e.g., snoring, breathing,breathing problems (such as sleep apnea) User's voice Phonocardiography(pulse arrival time, pulse transmit time, pulse wave velocity) Straingauge Heart Rate Example: Heart Rate Variability In a wrist band StressWet/Immersion Sensor Stress Example Sensor: Swimming detection Galvanicskin response Shower detection

There are numerous technologies that could be used to measure proximalor distal pulse arrival time. These include: photoplethysmography (PPG),Electroardiography (EKG/ECG), phonocardiography (PCG),ballistocardiography (BCG), impedance plethysmography or impedancephlebography (IPG), and ultrasound. These technologies can be combinedin different ways to acquire a PTT measurement or estimate.

In one example embodiment, the biometric monitoring device may includean optical sensor to detect, sense, sample and/or generate data that maybe used to determine information representative of, for example, stress(or level thereof), blood pressure, and/or heart rate of a user. (See,for example, FIGS. 9A through 10C and 18A through #18G). In suchembodiments, the biometric monitoring device may include an opticalsensor having one or more light sources (LED, laser, etc.) to emit oroutput light into the user's body, as well as light detectors(photodiodes, phototransistors, etc.) to sample, measure and/or detect aresponse or reflection of such light from the user's body and providedata used to determine data that is representative of stress (or levelthereof), blood pressure, and/or heart rate of a user (e.g., such as byusing photoplethysmography).

In one example embodiment, a user's pulse waveform measurement or heartrate measurement may be triggered by criteria determined by one or moresensors (or processing circuitry connected to them). For instance, whendata from a motion sensor(s) indicates a period of stillness or oflittle motion, the biometric monitoring device may trigger, acquire,and/or obtain a pulse waveform or heart rate measurement or data. (See,for example, FIGS. 16, 19A, and 19B).

FIG. 19A illustrates an example of a portable biometric monitoringdevice having a heart rate or PPG sensor, motion sensor, display,vibromotor, and communication circuitry which is connected to aprocessor.

FIG. 19B illustrates an example of a portable biometric monitoringdevice having a heart rate or PPG sensor, motion sensor, display,vibromotor, location sensor, altitude sensor, skin conductance/wetsensor and communication circuitry which is connected to a processor.

In one embodiment, when the motion sensor(s) indicate user activity ormotion (for example, motion that is not suitable or optimum to trigger,acquire, and/or obtain desired pulse waveform or heart rate measurementor data (for example, data used to determine a user's resting heartrate)), the biometric monitoring device and/or the sensor(s) employed toacquire and/or obtain a desired pulse waveform or heart rate measurementor data may be placed in, or remain in, a low power state. Since pulsewaveform or heart rate measurements taken during motion may be lessreliable and may be corrupted by motion artifacts, it may be desirableto decrease the frequency with which pulse waveform data samples arecollected (thus decreasing power usage) when the biometric monitoringdevice is in motion.

In another embodiment, a biometric monitoring device may employ data(for example, from one or more motion sensors) indicative of useractivity or motion to adjust or modify characteristics of triggering,acquiring, and/or obtaining desired pulse waveform or heart ratemeasurements or data (for example, to improve robustness to motionartifact). For instance, if the biometric monitoring device receivesdata indicative of user activity or motion, the biometric monitoringdevice may adjust or modify the sampling rate and/or resolution mode ofsensors used to acquire pulse waveform data (for example, where theamount of user motion exceeds a certain threshold, the biometricmonitoring device may increase the sampling rate and/or increase thesampling resolution mode of sensors employed to acquire pulse waveformor heart rate measurement or data.) Moreover, the biometric monitoringdevice may adjust or modify the sampling rate and/or resolution mode ofthe motion sensor(s) during such periods of user activity or motion (forexample, periods where the amount of user motion exceeds a certainthreshold). In this way, when the biometric monitoring device determinesor detects such user activity or motion, the biometric monitoring devicemay place the motion sensor(s) into a higher sampling rate and/or highersampling resolution mode to, for example, enable more accurate adaptivefiltering of the heart rate signal. (See, for example, FIG. 16).

FIG. 16 illustrates an example of a portable biometric monitoring devicethat changes how it detects a user's heart rate based on how muchmovement the biometric monitoring device is experiencing. In the casewhere there is motion detected (e.g., through the use of anaccelerometer), the user may be considered by the biometric monitoringdevice to be “active” and high-sampling-rate heart rate detection mayoccur to reduce motion artifacts in the pulse waveform measurement orheart rate measurement. This data may be saved and/or displayed. In thecase that the user is determined by the biometric monitoring device tonot be moving (or to be relatively sedentary), low-sampling-rate heartrate detection (which does not consume as much power) may be adequate tomeasure a heart rate and may thus be used.

Notably, where a biometric monitoring device employs optical techniquesto acquire pulse waveform or heart rate measurements or data, e.g., byusing photoplethysmography, a motion signal may be employed to determineor establish a particular approach or technique to data acquisition ormeasurement by the heart rate or pulse wave sensor (e.g., synchronousdetection rather than a non-amplitude-modulated approach) and/oranalysis thereof. (See, for example, FIG. 18E). In this way, the datawhich is indicative of the amount of user motion or activity may causethe biometric monitoring device to establish or adjust the type ortechnique of data acquisition or measurement used by an optical pulsewaveform sensor or sensors.

For example, in one embodiment, a biometric monitoring device (orheart-rate measurement technique as disclosed herein may adjust and/orreduce the sampling rate of optical heart rate sampling when motiondetector circuitry detects or determines that the biometric monitoringdevice wearer's motion is below a threshold (for example, if thebiometric monitoring device determines the user is sedentary or asleep).(See, for example, FIG. 16). In this way, the biometric monitoringdevice may control its power consumption. For example, the biometricmonitoring device may reduce power consumption by reducing the sensorsampling rate—for instance, the biometric monitoring device may samplethe heart rate (via the pulse waveform sensor) once every 10 minutes, or10 seconds out of every 1 minute. Notably, the biometric monitoringdevice may, in addition thereto or in lieu thereof, control powerconsumption via controlling data processing circuitry analysis and/ordata analysis techniques in accordance with motion detection. As such,the motion of the user may impact the heart rate or pulse waveform dataacquisition parameters and/or data analysis or processing thereof.

Motion Artifact Suppression in Pulse Waveform Sensors

As discussed above, the raw heart rate signal measured by a PPG sensormay be improved by using one or more algorithms to remove motionartifacts. Movement of the user (for determining motion artifacts) maybe measured using sensors including, but not limited to, accelerometers,gyroscopes, proximity detectors, magnetometers, etc. The goal of suchalgorithms is to remove components os the PPG signal attributable tomovement (movement artifacts) using the movement signal captured fromthe other sensors as a guide. In one embodiment the movement artifactsin the PPG signal may be removed using an adaptive filter based on ahybrid Kalman filter and a least mean square filter or a recursive leastsquares filter. The heart rate may then be extracted from thecleaned/filtered signal using a peak counting algorithm or a powerspectral density estimation algorithm. Alternatively, a Kalman filter orparticle filter may be used to remove such movement artifacts.

Another approach that may be used to calculate the heart rate frequencyis to create a model of the heart rate signal as Y=Y_(dc)Σa_(k)*coskθ+b_(k)*sin kθ, where k is the order of harmonic components, and θ is amodel parameter for heart rate. This model may then be fit to the signalusing either an extended Kalman filter or a particle filter. This modelexploits the fact that the signal is not sinusoidal so contains powerboth at the fundamental harmonic as well as multiple additionalharmonics.

Alternately, the signal may be modeled asY=Y_(dc)Σa_(k)*sin(k*w_(motion)t+θ)+Σb_(k)*sin(k*w_(HR)t+Ø), wherew_(motion) is estimated directly from the accelerometer signal (oranother motion sensor signal).

Sedentary, Sleep, and Active Classified Metrics

In yet another example embodiment, the biometric monitoring device mayemploy sensors to calculate heart rate variability when the devicedetermines the user to be sedentary or asleep. Here, the biometricmonitoring device may operate the sensors in a higher-rate sampling mode(relative to non-sedentary periods or periods of user activity thatexceed a predetermined threshold) to calculate heart rate variability.The biometric monitoring device (or an external device) may employ heartrate variability as an indicator of cardiac health or stress.

Indeed, in some embodiments, the biometric monitoring device may measureand/or determine the user's stress level and/or cardiac health when theuser is sedentary and/or asleep (for example, as detected and/ordetermined by the biometric monitoring device). Some embodiments of abiometric monitoring device of the present disclosure may determine theuser's stress level, health state (e.g., risk, onset, or progression offever or cold), and/or cardiac health using sensor data that isindicative of the heart rate variability, galvanic skin response, skintemperature, body temperature, and/or heart rate. In this way,processing circuitry of the biometric monitoring device may determineand/or track the user's “baseline” stress levels over time and/orcardiac “health” over time. In another embodiment, the device maymeasure a physiologic parameter of the user during one or more periodswhere the user is motionless (or the user's motion is below apredetermined threshold), such as when the user is sitting, lying down,asleep, or in a sleep stage (e.g., deep sleep). Such data may also beemployed by the biometric monitoring device as a “baseline” forstress-related parameters, health-related parameters (e.g., risk oronset of fever or cold), cardiac health, heart rate variability,galvanic skin response, skin temperature, body temperature and/or heartrate.

Sleep Monitoring

In some embodiments, the biometric monitoring device may automaticallydetect or determine when the user is attempting to go to sleep, isentering sleep, is asleep, and/or is awoken from a period of sleep. Insuch embodiments, the biometric monitoring device may employphysiological sensors to acquire data and the data processing circuitryof the biometric monitoring device may correlate a combination of heartrate, heart rate variability, respiration rate, galvanic skin response,motion, skin temperature, and/or body temperature data collected fromsensors of the biometric monitoring device to detect or determine if theuser is attempting to go to sleep, is entering sleep, is asleep, and/oris awoken from a period of sleep. In response, the biometric monitoringdevice may, for example, acquire physiological data (of the types, andin the manners, as described herein) and/or determine physiologicalconditions of the user (of the types, and in the manners, as describedherein). For example, a decrease or cessation of user motion combinedwith a reduction in user heart rate and/or a change in heart ratevariability may indicate that the user has fallen asleep. Subsequentchanges in heart rate variability and galvanic skin response may then beused by the biometric monitoring device to determine transitions of theuser's sleep state between two or more stages of sleep (for example,into lighter and/or deeper stages of sleep). Motion by the user and/oran elevated heart rate and/or a change in heart rate variability may beused by the biometric monitoring device to determine that the user hasawoken.

Real-time, windowed, or batch processing to maybe used to determine thetransitions between wake, sleep, and sleep stages. For instance, adecrease in heart rate may be measured in a time window where the heartrate is elevated at the start of the window and reduced in the middle(and/or end) of the window. The awake and sleep stages may be classifiedby a hidden Markov model using changes in motion signal (e.g.,decreasing motion intensity), heart rate, heart rate variability, skintemperature, galvanic skin response, and/or ambient light levels. Thetransition points may be determined through a changepoint algorithm(e.g., Bayesian changepoint analysis). The transition between awake andsleep may be determined by observing periods where the user's heart ratedecreases over a predetermined time duration by at least a certainthreshold but within a predetermined margin of the user's resting heartrate (that is observed as, for example, the minimum heart rate of theuser while sleeping). Similarly, the transition between sleep and awakemay be determined by observing an increase in the user's heart rateabove a predetermined threshold of the user's resting heart rate.

In some embodiments, the biometric monitoring device may be onecomponent of a system for monitoring sleep, where the system includes asecondary device configured to communicate with the biometric monitoringdevice and adapted to be placed near the sleeper (e.g., an alarm clock).The secondary device may, in some implementations, have a shape andmechanical and/or magnetic interface to accept the biometric monitoringdevice for safe keeping, communication, and/or charging. However, thesecondary device may also be generic to the biometric monitoring device,e.g., a smartphone that is not specifically designed to physicallyinterface with the biometric monitoring device. The communicationbetween the biometric monitoring device and the secondary device may beprovided through wired communication interfaces or through wirelesscommunication interfaces and protocols such as Bluetooth (including, forexample, Bluetooth 4.0 and Bluetooth Low Energy protocols), RFID, NFC,or WLAN. The secondary device may include sensors to assist in sleepmonitoring or environmental monitoring such as, for example, sensorsthat measure ambient light, noise and/or sound (e.g., to detectsnoring), temperature, humidity, and air quality (pollen, dust, CO2,etc.). In one embodiment, the secondary device may communicate with anexternal service such as www.fitbit.com or a server (e.g., a personalcomputer). Communication with the secondary device may be achievedthrough wired (e.g., Ethernet, USB) or wireless (e.g., WLAN, Bluetooth,RFID, NFC, cellular) circuitry and protocols to transfer data to and/orfrom the secondary device. The secondary device may also act as a relayto transfer data to and/or from the biometric monitoring device toand/or from an external service such as www.fitbit.com or other service(e.g., data such as news, social network updates, email, calendarnotifications) or server (e.g., personal computer, mobile phone,tablet). Calculation of the user's sleep data may be performed on one orboth devices or an external service (e.g., a cloud server) using datafrom one or both devices.

The secondary device may be equipped with a display to display dataobtained by the secondary device or data transferred to it by thebiometric monitoring device, the external service, or a combination ofdata from the biometric monitoring device, the secondary device, and/orthe external service. For example, the secondary device may display dataindicative of the user's heart rate, total steps for the day, activityand/or sleep goal achievement, the day's weather (measured by thesecondary device or reported for a location by an external service),etc. In another example, the secondary device may display data relatedto the ranking of the user relative to other users, such as total weeklystep count. In yet another embodiment, the biometric monitoring devicemay be equipped with a display to display data obtained by the biometricmonitoring device, the secondary device, the external service, or acombination of the three sources. In embodiments where the first deviceis equipped with a wakeup alarm (e.g., vibramotor, speaker), thesecondary device may act as a backup alarm (e.g., using an audiospeaker). The secondary device may also have an interface (e.g., displayand buttons or touch screen) to create, delete, modify, or enable alarmson the first and/or the secondary device.

Sensor-Based Standby Mode

In another embodiment, the biometric monitoring device may automaticallydetect or determine whether it is or is not attached to, disposed on,and/or being worn by a user. In response to detecting or determiningthat the biometric monitoring device is not attached to, disposed on,and/or being worn by a user, the biometric monitoring device (orselected portions thereof) may implement or be placed in a low powermode of operation—for example, the optical pulse waveform sensor and/orcircuitry may be placed in a lower power or sleep mode. For example, inone embodiment, the biometric monitoring device may include one or morelight detectors (photodiodes, phototransistors, etc.). If, at a givenlight intensity setting (for example, with respect to the light emittedby a light source that is part of the biometric monitoring device), oneor more light detectors provides a low return signal, the biometricmonitoring device may interpret the data as indicative of the device notbeing worn. Upon such a determination, the device may reduce its powerconsumption—for example, by “disabling” or adjusting the operatingconditions of the stress and/or heart rate detection sensors and/orcircuitry in addition to other device circuitry or displays (forexample, by reducing the duty cycle of or disabling the light source(s)and/or detector(s), turning off the device display, and/or disabling orattenuating associated circuitry or portions thereof). In addition, thebiometric monitoring device may periodically determine (e.g., once persecond) if the operating conditions of the stress and/or heart ratedetection sensors and/or associated circuitry should be restored to anormal operating condition (for example, light source(s), detector(s)and/or associated circuitry should return to a normal operating mode forheart rate detection). In another embodiment, the biometric monitoringdevice may restore the operating conditions of the stress and/or heartrate detection sensors and/or associated circuitry upon detection of atriggerable event—for example, upon detecting motion of the device (forexample, based on data from one or more motion sensor(s)) and/ordetecting a user input via the user interface (for example, a tap, bumpor swipe interaction with the biometric monitoring device). In somerelated embodiments, the biometric monitoring device may, for powersaving purposes, reduce its default rate of pulse waveform measurementor heart rate measurement collection to, for instance, one measurementper minute while the user is not highly active and the user may have theoption of putting the device into a mode of operation to generatemeasurements on demand or at a faster rate (e.g., once per second), forinstance, by pushing a button.

Optical Sensor(s)

In one embodiment, the optical sensors (sources and/or detectors) may bedisposed on an interior or skin-side of the biometric monitoring device(i.e., a side of the biometric monitoring device that contacts, touches,and/or faces the skin of the user (hereinafter “skin-side”). (See, forexample, FIGS. 9A through 10C). In another embodiment, the opticalsensors may be disposed on one or more sides of the device, includingthe skin-side and one or more sides of the device that face or areexposed to the ambient environment (environmental side). (See, forexample, FIGS. 13A through 14).

Optical sensors such as PPG sensors may be used to obtain data that canbe analyzed to obtain pulse waveforms or heartbeat waveform. The dataused for obtaining pulse waveform for PWA may be collected underdifferent operational mode than data used for heartbeat analysis. Forexample, PWA data in some implementations require higher samplingfrequency. Furthermore, pulse wave analysis may require morphologicalfeatures that are necessary in heartbeat analysis.

FIG. 13A illustrates an example of a portable monitoring device having aband; optical sensors and light emitters may be placed on the band.

FIG. 13B illustrates an example of a portable biometric monitoringdevice having a display and wristband. Additionally, optical PPG (e.g.,heart rate) detection sensors and/or emitters may be located on the sideof the biometric monitoring device. In one embodiment, these may belocated in side-mounted buttons.

FIG. 14 depicts a user pressing the side of a portable biometricmonitoring device to take a pulse waveform measurement or heart ratemeasurement from a side-mounted optical heart rate detection sensor. Thedisplay of the biometric monitoring device may show whether or not theheart rate has been detected and/or display the user's heart rate.

Notably, the data from such optical sensors may be representative ofphysiological data and/or environmental data. Indeed, in one embodiment,the optical sensors provide, acquire and/or detect information frommultiple sides of the biometric monitoring device whether or not thesensors are disposed on one or more of the multiple sides. For example,the optical sensors may obtain data related to the ambient lightconditions of the environment.

Where optical sensors are disposed or arranged on the skin-side of thebiometric monitoring device, in operation, a light source in thebiometric monitoring device may emit light upon the skin of the userand, in response, a light detector in the biometric monitoring devicemay sample, acquire, and/or detect corresponding reflected and/oremitted light from the skin (and from inside the body). The one or morelight sources and light detectors may be arranged in an array or patternthat enhances or optimizes the signal-to-noise ratio and/or serves toreduce or minimize power consumption by the light sources and lightdetectors. These optical sensors may sample, acquire and/or detectphysiological data which may then be processed or analyzed (for example,by resident processing circuitry) to obtain data that is representativeof, for example, a user's heart rate, respiration, heart ratevariability, oxygen saturation (SpO₂), blood volume, blood glucose, skinmoisture, and/or skin pigmentation level.

The light source(s) may emit light having one or more wavelengths thatare specific or directed to a type of physiological data to becollected. Similarly, the optical detectors may sample, measure and/ordetect one or more wavelengths that are also specific or directed to atype of physiological data to be collected and/or a physiologicalparameter (of the user) to be assessed or determined. For instance, inone embodiment, a light source emitting light having a wavelength in thegreen spectrum (for example, an LED that emits light having wavelengthscorresponding to the green spectrum) and a photodiode positioned tosample, measure, and/or detect a response or reflection correspondingwith such light may provide data that may be used to determine or detectheart rate. In contrast, a light source emitting light having awavelength in the red spectrum (for example, an LED that emits lighthaving wavelengths corresponding to the red spectrum) and a light sourceemitting light having a wavelength in the infrared spectrum (forexample, an LED that emits light having wavelengths corresponding to theIR spectrum) and photodiode positioned to sample, measure and/or detecta response or reflection of such light may provide data used todetermine or detect SpO₂.

Indeed, in some embodiments, the color or wavelength of the lightemitted by the light source, e.g., an LED (or set of LEDs), may bemodified, adjusted, and/or controlled in accordance with a predeterminedtype of physiological data being acquired or conditions of operation.Here, the wavelength of the light emitted by the light source may beadjusted and/or controlled to optimize and/or enhance the “quality” ofthe physiological data obtained and/or sampled by the detector. Forexample, the color of the light emitted by the LED may be switched frominfrared to green when the user's skin temperature or the ambienttemperature is cool in order to enhance the signal corresponding tocardiac activity. (See, for example, FIG. 18D).

The biometric monitoring device, in some embodiments, may include awindow (for example, a window that is, to casual inspection, opaque) inthe housing to facilitate optical transmission between the opticalsensors and the user. Here, the window may permit light (for example, ofa selected wavelength) to be emitted by, for example, one or more LEDs,onto the skin of the user and a response or reflection of that light topass back through the window to be sampled, measured, and/or detectedby, for example, one or more photodiodes. In one embodiment, thecircuitry related to emitting and receiving light may be disposed in theinterior of the device housing and underneath or behind a plastic orglass layer (for example, painted with infrared ink) or an infrared lensor filter that permits infrared light to pass but not light in the humanvisual spectrum. In this way, the light transmissivity of the window maybe invisible to the human eye.

The biometric monitoring device may employ light pipes or otherlight-transmissive structures to facilitate transmission of light fromthe light sources to the user's body and skin. (See, for example, FIGS.11A through 12). In this regard, in some embodiments, light may bedirected from the light source to the skin of the user through suchlight pipes or other light-transmissive structures. Scattered light fromthe user's body may be directed back to the optical circuitry in thebiometric monitoring device through the same or similar structures.Indeed, the light-transmissive structures may employ a material and/oroptical design to facilitate low light loss (for example, thelight-transmissive structures may include a lens to facilitate lightcollection, and portions of the light-transmissive structures may becoated with or adjacent to reflective materials to promote internalreflection of light within the light-transmissive structures) therebyimproving the signal-to-noise-ratio of the photo detector and/orfacilitating reduced power consumption of the light source(s) and/orlight detectors. In some embodiments, the light pipes or otherlight-transmissive structures may include a material that selectivelytransmits light having one or more specific or predetermined wavelengthswith higher efficiency than others, thereby acting as a bandpass filter.Such a bandpass filter may be tuned to improve the signal of a specificphysiological data type. For example, in one embodiment, anIn-Mold-Labeling or “IML” light-transmissive structure may beimplemented wherein the light-transmissive structure uses a materialwith predetermined or desired optical characteristics to create aspecific bandpass characteristic, for example, so as to pass infraredlight with greater efficiency than light of other wavelengths (forexample, light having a wavelength in human visible spectrum). Inanother embodiment, a biometric monitoring device may employ alight-transmissive structure having an optically opaque portion(including certain optical properties) and an optically-transparentportion (including optical properties different from theoptically-opaque portion). Such a light-transmissive structure may beprovided via a double-shot or two-step molding process wherein opticallyopaque material and optically transparent material are separatelyinjected into a mold. A biometric monitoring device implementing such alight-transmissive structure may include different light transmissivityproperties for different wavelengths depending on the direction of lighttravel through the light-transmissive structure. For example, in oneembodiment, the optically-opaque material may be reflective to aspecific wavelength range so as to more efficiently transport light fromthe user's body back to the light detector (which may be of a differentwavelength(s) relative to the wavelength(s) of the emitted light).

In another embodiment, reflective structures may be placed in the fieldof view of the light emitter(s) and/or light detector(s). For example,the sides of holes that channel light from light emitter(s) to a user'sskin and/or from the user's skin to light detector(s) (or through whichlight-transmissive structures that perform such channeling travel) maybe covered in a reflective material (e.g., chromed) to facilitate lighttransmission. The reflective material may increase the efficiency withwhich the light is transported to the skin from the light source(s) andthen from the skin back into the detector(s). The reflectively-coatedhole may be filled in with an optical epoxy or other transparentmaterial to prevent liquid from entering the device body while stillallowing light to be transmitted with low transmission loss.

In another embodiment that implements light-transmissive structures (forexample, structures created or formed through IML), suchlight-transmissive structures may include a mask consisting of an opaquematerial that limits the aperture of one, some, or all of the lightsource(s) and/or detector(s). In this way, the light-transmissivestructures may selectively “define” a preferential volume of the user'sbody that light is emitted into and/or detected from. Notably, othermask configurations may be employed or implemented in connection withthe concepts described and/or illustrated herein; all such maskingconfigurations to, for example, improve the photoplethysmography signaland which are implemented in connection with the concepts describedand/or illustrated herein are intended to fall within the scope of thepresent disclosure.

In another embodiment, the light emitter(s) and/or detector(s) may beconfigured to transmit light through a hole or series of holes in thedevice exterior. This hole or series of holes may be filled in withlight-transmissive epoxy (e.g. optical epoxy). The epoxy may form alight pipe that allows light to be transmitted from the light emitter(s)to the skin and from the skin back into the light detector(s). Thistechnique also has the advantage that the epoxy may form a watertightseal, preventing water, sweat or other liquid from entering the devicebody though the hole(s) on the device exterior that allow the lightemitter(s) and detector(s) to transmit to, and receive light from, thebiometric monitoring device body exterior. An epoxy with a high thermalconductivity may be used to help prevent the light source(s) (e.g.,LED's) from overheating.

In any of the light-transmissive structures described herein, theexposed surfaces of the optics (light-transmissive structures) or devicebody may include a hard coat paint, hard coat dip, or optical coatings(such as anti-reflection, scratch resistance, anti-fog, and/orwavelength band block (such as ultraviolet light blocking) coatings).Such characteristics or materials may improve the operation, accuracyand/or longevity of the biometric monitoring device.

FIG. 11A illustrates an example of one potential PPG light source andphotodetector geometry. In this embodiment, two light sources are placedon either side of a photodetector. These three devices are located in aprotrusion on the back of a wristband-type biometric monitoring device(the side which faces the skin of the user).

FIGS. 11B and 11C illustrate examples of a PPG sensor having aphotodetector and two LED light sources. These components are placed ina biometric monitoring device that has a protrusion on the back side.Light pipes optically connect the LEDs and photodetector with thesurface of the user's skin. Beneath the skin, the light from the lightsources scatters off of blood in the body, some of which may bescattered or reflected back into the photodetector.

FIG. 12 Illustrates an example of a biometric monitoring device with anoptimized PPG detector that has a protrusion with curved sides so as notto discomfort the user. Additionally, the surface of light pipes thatoptically couple the photodetector and the LEDs to the wearer's skin arecontoured to maximize light flux coupling between the LEDs andphotodetectors and the light pipes. The ends of the light pipes thatface the user's skin are also contoured. This contour may focus ordefocus light to optimize the PPG signal. For example, the contour mayfocus emitted light to a certain depth and location that coincides withan area where blood flow is likely to occur. The vertex of these focimay overlap or be very close together so that the photodetector receivesthe maximum possible amount of scattered light.

In some embodiments, the biometric monitoring device may include aconcave or convex shape, e.g., a lens, on the skin-side of the device,to focus light towards a specific volume at a specific depth in the skinand increase the efficiency of light collected from that point into thephotodetector. (See, for example, FIGS. 11A through 12). Where such abiometric monitoring device also employs light pipes to selectively andcontrollably route light, it may be advantageous to shape the end of thelight pipe with a degree of cylindricity, e.g., the end of the lightpipe may be a be a cylindrical surface (or portion thereof) defined by acylinder axis that is nominally parallel to the skin-side (for example,rather than use an axially-symmetric lens). For example, in awristband-style biometric monitoring device, such a cylindrical lens maybe oriented such that the cylinder axis is nominally parallel to thewearer's forearm, which may have the effect of limiting the amount oflight that enters such a lens from directions parallel to the person'sforearm and increasing the amount of light that enters such a lens fromdirections perpendicular to the person's forearm—since ambient light ismore likely to reach the sensor detection area from directions that arenot occluded by the straps of the biometric monitoring device, i.e.,along the user's forearm axis, than from directions that are occluded bythe straps, i.e., perpendicular to the user's forearm. Such aconfiguration may improve the signal-to-noise-ratio by increasing theefficiency of light transferred from the emitter onto or into the skinof the user while decreasing “stray” light from being detected orcollected by the photodetector. In this way, the signal sampled,measured and/or detected by the photodetector consists less of straylight and more of the user's skin/body response to such emitted light(signal or data that is representative of the response to the emittedlight).

In another embodiment, light-transmissive epoxy may be molded into aconcave or convex shape so as to provide beneficial optical propertiesto sensors as well. For example, during the application of lighttransmissive epoxy, the top of the light-transmissive structure that isformed by the epoxy may be shaped into a concave surface so that lightcouples more effectively into the light-transmissive structure.

In one embodiment, the components of the optical sensor may bepositioned on the skin-side of the device and arranged or positioned toreduce or minimize the distance between (i) the light source(s) and/orthe associated detector(s) and (ii) the skin of the user. See, forexample, FIG. 10A, which provides a cross-sectional view of a sensorprotrusion of an example portable monitoring device. In FIG. 10A, twolight sources (e.g., LEDs) are placed on either side of a photodetectorto enable PPG sensing. A light-blocking material is placed between thelight sources and the photodetector to prevent any light from the lightsources from reaching photodetector without first exiting the body ofthe biometric monitoring device. A flexible transparent layer may beplaced on the lower surface of the sensor protrusion to form a seal.This transparent layer may serve other functions such as preventingliquid from entering the device where the light sources orphotodetectors are placed. This transparent layer may be formed throughin-mold labeling or “IML”. The light sources and photodetector may beplaced on a flexible PCB.

Such a configuration may improve the efficiency of light flux couplingbetween the components of the optical sensor and the user's body. Forexample, in one embodiment, the light source(s) and/or associateddetector(s) may be disposed on a flexible or pliable substrate that mayflex, allowing the skin-side of the biometric monitoring device, whichmay be made from a compliant material, to conform (for example, withoutadditional processing) or be capable of being shaped (or compliant) toconform to the shape of the body part (for example, the user's wrist,arm, ankle, and/or leg) to which the biometric monitoring device iscoupled to or attached during normal operation so that the lightsource(s) and/or associated detector(s) are/is close to the skin of theuser (i.e., with little to no gap between the skin-side of the deviceand the adjacent surface of the skin of the user. See, for example, FIG.13A. In one embodiment, the light source(s) and/or associateddetector(s) may be disposed on a Flat Flex Cable or “FFC” or flexiblePCB. In this embodiment, the flexible or pliable substrate (for example,an FFC or flexible PCB) may connect to a second substrate (for example,PCB) within the device having other components disposed thereon (forexample, the data processing circuitry). Optical components of differingheights may be mounted to different “fingers” of flexible substrate andpressed or secured to the housing surface such that the opticalcomponents are flush to the housing surface. In one embodiment, thesecond substrate may be a relatively inflexible or non-pliablesubstrate, fixed within the device, having other circuitry andcomponents (passive and/or active) disposed thereon.

FIG. 10B depicts a cross-sectional view of a sensor protrusion of anexample portable monitoring device; this protrusion is similar to thatpresented in FIG. 10A with the exception that the light sources andphotodetector are placed on a flat and/or rigid PCB.

FIG. 10C provides another cross-sectional view of an example PPG sensorimplementation. Of note in this PPG sensor is the lack of a protrusion.Additionally, a liquid gasket and/or a pressure sensitive adhesive areused to prevent liquid from entering the biometric monitoring devicebody.

Some embodiments of biometric monitoring devices may be adapted to beworn or carried on the body of a user. In some embodiments including theoptical pulse waveform monitor or heart rate monitor, the device may bea wrist-worn or arm-mounted accessory such as a watch or bracelet. (See,for example, FIGS. 9A through 14). In one embodiment, optical elementsof the optical pulse waveform monitor or heart rate monitor may belocated on the interior or skin-side of the biometric monitoring device,for example, facing the top of the wrist (i.e., the optical pulsewaveform monitor or heart rate monitor may be adjacent to and facing thewrist) when the biometric monitoring device is worn on the wrist. (See,for example, FIGS. 9A through 10C).

In another embodiment, the optical pulse waveform monitor or heart ratemonitor may be located on one or more external or environmental sidesurfaces of the biometric monitoring device. (See, for example, FIGS.13B and 14). In such embodiments, the user may touch an optical window(behind which optical elements of the optical pulse waveform monitor orheart rate monitor are located) with a finger on the opposing hand toinitiate a pulse waveform measurement or heart rate measurement (and/orother metrics related to heart rate such as heart rate variability)and/or collect data which may be used to determine the user's heart rate(and/or other metrics related to heart rate). (See, for example, FIG.13B). In one embodiment, the biometric monitoring device may trigger orinitiate the measurement(s) by detecting a (sudden) drop in incidentlight on the photodiode—for example, when the user's finger is placedover the optical window. In addition thereto, or in lieu thereof, apulse waveform measurement or heart rate measurement (or other suchmetric) may be trigged by an IR-based proximity detector and/orcapacitive touch/proximity detector (which may be separate from otherdetectors). Such IR-based proximity detector and/or capacitivetouch/proximity detector may be disposed in or on and/or functionally,electrically and/or physically coupled to the optical window to detector determine the presence of, for example, the user's finger.

In yet another embodiment, the biometric monitoring device may include abutton that, when depressed, triggers or initiates pulse waveformmeasurement or heart rate measurement (and/or other metrics related toheart rate). The button may be disposed in close proximity to theoptical window to facilitate the user pressing the button while thefinger is disposed on the optical window. (See, for example, FIG. 14).In one embodiment, the optical window may be embedded in a push button.Thus, when the user presses the button, it may trigger a measurement ofthe finger that depresses the button. Indeed, the button may be given ashape and/or resistance to pressing that enhances or optimizes apressure profile of the button against the finger to provide a highsignal-to-noise-ratio during measurement or data acquisition. In otherembodiments (not illustrated), the biometric monitoring device may takethe form of a clip, a smooth object, a pendant, an anklet, a belt, etc.that is adapted to be worn on the body, clipped or mounted to an articleof clothing, deposited in clothing (e.g., in a pocket), or deposited inan accessory (e.g., handbag).

In one specific embodiment, the biometric monitoring device may includea protrusion on the skin- or interior side of the device. (See, FIGS. 9Athrough 13A). When coupled to the user, the protrusion may engage theskin with more force than the surrounding device body. In thisembodiment, an optical window or light transmissive structure (both ofwhich are discussed in detail above) may form or be incorporated in aportion of the protrusion. The light emitter(s) and/or detector(s) ofthe optical sensor may be disposed or arranged in the protrusion nearthe window or light transmissive structure. (See, for example, FIGS. 9Band 13A). As such, when attached to the user's body, the window portionof the protrusion of the biometric monitoring device may engage theuser's skin with more force than the surrounding device body—therebyproviding a more secure physical coupling between the user's skin andthe optical window. That is, the protrusion may cause sustained contactbetween the biometric monitoring device and the user's skin that mayreduce the amount of stray light measured by the photodetector, decreaserelative motion between the biometric monitoring device and the user,and/or provide improved local pressure to the user's skin; all of whichmay increase the quality of the cardiac signal of interest. Notably, theprotrusion may contain other sensors that benefit from close proximityand/or secure contact to the user's skin. These may be included inaddition to or in lieu of a pulse waveform sensor and include sensorssuch as a skin temperature sensor (e.g., noncontact thermopile thatutilizes the optical window or thermistor joined with thermal epoxy tothe outer surface of the protrusion), pulse oximeter, blood pressuresensor, EMG, or galvanic skin response (GSR) sensor.

In addition thereto, or in lieu thereof, a portion of the skin-side ofthe biometric monitoring device may include a friction enhancingmechanism or material. For example, the skin-side of the biometricmonitoring device may include a plurality of raised or depressed regionsor portions (for example, small bumps, ridges, grooves, and/or divots).Moreover, a friction enhancing material (for example, a gel-likematerial such as silicone or other elastomeric material) may be disposedon the skin-side. Indeed, a device back made out of gel may also providefriction while also improving user comfort and preventing stray lightfrom entering. As noted above, a friction-enhancing mechanism ormaterial may be used alone or in conjunction with the biometricmonitoring device having a protrusion as described herein. In thisregard, the biometric monitoring device may include a plurality ofraised or depressed regions portions (for example, small bumps, ridges,grooves, and/or divots) in or on the protrusion portion of the device.Indeed, such raised or depressed regions portions may beincorporated/embedded into or on a window portion of the protrusion. Inaddition thereto, or in lieu thereof, the protrusion portion may consistof or be coated with a friction enhancing material (for example, agel-like material such as silicone). Notably, the use of a protrusionand/or friction may improve measurement accuracy of data acquisitioncorresponding to certain parameters (e.g., heart rate, heart ratevariability, galvanic skin response, skin temperature, skin coloration,heat flux, blood pressure, blood glucose, etc.) by reducing motion ofthe biometric monitoring device (and thus of the sensor) relative to theuser's skin during operation, especially while the user is in motion.

Some or all of the interior or skin-side housing of the biometricmonitoring device may also consist of a metal material (for example,steel, stainless steel, aluminum, magnesium, or titanium). Such aconfiguration may provide a structural rigidity. (See, for example, FIG.9B). In such an embodiment, the device body may be designed to behypoallergenic through the use of a hypoallergenic “nickel-free”stainless steel. Notably, it may be advantageous to employ (at least incertain locations) a type of metal that is at least somewhat ferrous(for example, a grade of stainless steel that is ferrous). In suchembodiments, the biometric monitoring device (where it includes arechargeable energy source (for example, rechargeable battery)) mayinterconnect with a charger via a connector that secures itself to thebiometric monitoring device using magnets that couple to the ferrousmaterial. In addition, biometric monitoring device may also engage adock or dock station, using such magnetic properties, to facilitate datatransfer. Moreover, such a housing may provide enhanced electromagneticshielding that would enhance the integrity and reliability of theoptical pulse waveform sensor and the pulse waveform data acquisitionprocess/operation. Furthermore, a skin temperature sensor may bephysically and thermally coupled, for example, with thermal epoxy, tothe metal body to sense the temperature of the user. In embodimentsincluding a protrusion, the sensor may be positioned near or in theprotrusion to provide secure contact and localized thermal coupling tothe user's skin.

In a preferred embodiment, one or more components of the optical sensor(which may, in one embodiment, be located in a protrusion, and/or inanother embodiment, may be disposed or placed flush to the surface ofthe biometric monitoring device) are attached, fixed, included, and/orsecured to the biometric monitoring device via a liquid-tight seal(i.e., a method/mechanism that prevents liquid ingress into the body ofthe biometric monitoring device). For example, in one embodiment, adevice back made out of a metal such as, but not limited to, stainlesssteel, aluminum, magnesium, or titanium, or from a rigid plastic mayprovide a structure that is stiff enough to maintain the structuralintegrity of the device while accommodating a watertight seal for thesensor package. (See, for example, FIGS. 9B through 10C).

In a preferred embodiment, a package or module of the optical sensor maybe connected to the device with a pressure-sensitive adhesive and aliquid gasket. See, for example, FIG. 10C, which provides anothercross-sectional view of a PPG sensor implementation. Of note in this PPGsensor is the lack of a protrusion. Additionally, a liquid gasket and/ora pressure sensitive adhesive are used to prevent liquid from enteringthe device body. Screws, rivets or the like may also be used, forexample, if a stronger or more durable connection is required betweenthe optical sensor package/module and the device body. Notably, thepresent embodiments may also use watertight glues, hydrophobic membranessuch as Gore-Tex, o-rings, sealant, grease, or epoxy to secure or attachthe optical sensor package/module to the biometric monitoring devicebody.

As discussed above, the biometric monitoring device may include amaterial disposed on the skin- or interior side that includes highreflectivity characteristics—for example, polished stainless steel,reflective paint, and polished plastic. In this way, light scattered offthe skin-side of the device may be reflected back into the skin in orderto, for example, improve the signal-to-noise-ratio of an optical pulsewaveform sensor. Indeed, this effectively increases the input lightsignal as compared with a device body back that is non-reflective (orless reflective). Notably, in one embodiment, the color of the skin orinterior side of the biometric monitoring device may be selected toprovide certain optical characteristics (for example, reflect certain orpredetermined wavelengths of light), in order to improve the signal withrespect to certain physiological data types. For example, where theskin- or interior side of the biometric monitoring is green, themeasurements of the heart rate may be enhanced due to the preferentialemission of a wavelength of the light corresponding to the greenspectrum. Where the skin- or interior side of the biometric monitoringis red, the measurements of the SpO₂ may be enhanced due to the emissionpreferential of a wavelength of the light corresponding to the redspectrum. In one embodiment, the color of the skin- or interior side ofthe biometric monitoring may be modified, adjusted and/or controlled inaccordance with a predetermined type of physiological data beingacquired.

FIG. 18A depicts an example schematic block diagram of an optical pulsewaveform sensor where light is emitted from a light source toward theuser's skin and the reflection of such light from the skin/internal bodyof the user is sensed by a light detector, the signal from which issubsequently digitized by an analog to digital converter (ADC). Theintensity of the light source may be modified (e.g., through a lightsource intensity control module) to maintain a desirable reflectedsignal intensity. For example, the light source intensity may be reducedto avoid saturation of the output signal from the light detector. Asanother example, the light source intensity may be increased to maintainthe output signal from the light detector within a desired range ofoutput values. Notably, active control of the system may be achievedthrough linear or nonlinear control methods such asproportional-integral-derivative (PID) control, fixed step control,predictive control, neural networks, hysteresis, and the like, and mayalso employ information derived from other sensors in the device such asmotion, galvanic skin response, etc. FIG. 18A is provided forillustration and does not limit the implementation of such a system to,for instance, an ADC integrated within a MCU, or the use of a MCU forthat matter. Other possible implementations include the use of one ormore internal or external ADCs, FPGAs, ASICs, etc.

In another embodiment, system with an optical pulse waveform sensor mayincorporate the use of a sample-and-hold circuit (or equivalent) tomaintain the output of the light detector while the light source isturned off or attenuated to save power. In embodiments where relativechanges in the light detector output are of primary importance (e.g.,pulse waveform measurement or heart rate measurement), thesample-and-hold circuit may not have to maintain an accurate copy of theoutput of the light detector. In such cases, the sample-and-hold may bereduced to, for example, a diode (e.g., Schottky diode) and capacitor.The output of the sample-and-hold circuit may be presented to an analogsignal conditioning circuit (e.g., a Sallen-Key bandpass filter, levelshifter, and/or gain circuit) to condition and amplify the signal withinfrequency bands of interest (e.g., 0.1 Hz to 10 Hz for cardiac orrespiratory function), which may then be digitized by the ADC. See, forexample, FIG. 18B.

In operation, circuit topologies such as those already described herein(e.g. a sample-and-hold circuit) remove the DC and low frequencycomponents of the signal and help resolve the AC component related toheart rate and/or respiration. The embodiment may also include theanalog signal conditioning circuitry for variable gain settings that canbe controlled to provide a suitable signal (e.g., not saturated). Theperformance characteristics (e.g., slew rate and/or gain bandwidthproduct) and power consumption of the light source, light detector,and/or sample-and-hold may be significantly higher than the analogsignal conditioning circuit to enable fast duty cycling of the lightsource. In some embodiments, the power provided to the light source andlight detector may be controlled separately from the power provided tothe analog signal conditioning circuit to provide additional powersavings. Alternatively or additionally, the circuitry can usefunctionality such as an enable, disable and/or shutdown to achievepower savings. In another embodiment, the output of the light detectorand/or sample-and-hold circuit may be sampled by an ADC in addition toor in lieu of the analog signal conditioning circuit to control thelight intensity of the light source or to measure the physiologicparameters of interest when, for example, the analog signal conditioningcircuit is not yet stable after a change to the light intensity setting.Notably, because the physiologic signal of interest is typically smallrelative to the inherent resolution of the ADC, in some embodiments, thereference voltages and/or gain of the ADC may be adjusted to enhancesignal quality and/or the ADC may be oversampled. In yet anotherembodiment, the device may digitize the output of only thesample-and-hold circuit by, for example, oversampling, adjusting thereference voltages and/or gain of the ADC, or using a high resolutionADC. See, for example, FIG. 18C.

PPG DC Offset Removal Techniques

In another embodiment, the sensor device may incorporate a differentialamplifier to amplify the relative changes in the output of the lightdetector. See, for example, FIG. 18F. In some embodiments, a digitalaverage or digital low-pass filtered signal may be subtracted from theoutput of the light detector. This modified signal may then be amplifiedbefore it is digitized by the ADC. In another embodiment, an analogaverage or analog low-pass filtered signal may be subtracted from theoutput of the light detector through, for example, the use of asample-and-hold circuit and analog signal conditioning circuitry. Thepower provided to the light source, light detector, and differentialamplifier may be controlled separately from the power provided to theanalog signal conditioning circuit to improve power savings.

In another embodiment, a signal (voltage or current, depending on thespecific sensor implementation) may be subtracted from the raw PPGsignal to remove any bias in the raw PPG signal and therefore increasethe gain or amplification of the PPG signal that contains heart rate (orother circulatory parameters such as heart rate variability)information. This signal may be set to a default value in the factory,to a value based on the user's specific skin reflectivity, absorption,and/or color, and/or may change depending on feedback from an ambientlight sensor, or depending on analytics of the PPG signal itself. Forexample, if the PPG signal is determined to have a large DC offset, aconstant voltage may be subtracted from the PPG signal to remove the DCoffset and enable a larger gain, therefore improving the PPG signalquality. The DC offset in this example may result from ambient light(for example from the sun or from indoor lighting) reaching thephotodetector from or reflected light from the PPG light source.

In another embodiment, a differential amplifier may be used to measurethe difference between current and previous samples rather than themagnitude of each signal. Since the magnitude of each sample istypically much greater than the difference between each sample, a largergain can be applied to each measurement, therefore improving the PPGsignal quality. The signal may then be integrated to obtain the originaltime domain signal.

In another embodiment, the light detector module may incorporate atransimpedance amplifier stage with variable gain. Such a configurationmay avoid or minimize saturation from bright ambient light and/or brightemitted light from the light source. For example, the gain of thetransimpedance amplifier may be automatically reduced with a variableresistor and/or multiplexed set of resistors in the negative feedbackpath of the transimpedance amplifier. In some embodiments, the devicemay incorporate little to no optical shielding from ambient light byamplitude-modulating the intensity of the light source and thendemodulating the output of the light detector (e.g., synchronousdetection). See, for instance, FIG. 18E. In other aspects, if theambient light is of sufficient brightness to obtain a heart rate signal,the light source may be reduced in brightness and/or turned offcompletely.

In yet another embodiment, the aforementioned processing techniques maybe used in combination to optically measure physiological parameters ofthe user. See, for example, FIG. 18G. This topology may allow the systemto operate in a low power measurement state and circuit topology whenapplicable and adapt to a higher power measurement state and circuittopology as necessary. For instance, the system may measure thephysiologic parameter (e.g., heart rate) of interest using analogsignal-conditioning circuitry while the user is immobile or sedentary toreduce power consumption, but switch to oversampled sampling of thelight detector output directly while the user is active.

In embodiments where the biometric monitoring device includes a pulsewaveform monitor or heart rate monitor, processing of the signal toobtain pulse waveform or heart rate measurements may include filteringand/or signal conditioning such as band-pass filtering (e.g.,Butterworth filter). To counteract large transients that may occur inthe signal and/or to improve convergence of said filtering, nonlinearapproaches may be employed such as neural networks or slew ratelimiting. Data from the sensors on the device such as motion, galvanicskin response, skin temperature, etc., may be used to adjust the signalconditioning methods employed. Under certain operating conditions, theheart rate of the user may be measured by counting the number of signalpeaks within a time window or by utilizing the fundamental frequency orsecond harmonic of the signal (e.g., through a fast Fourier transform(FFT)). In other cases, such as pulse waveform data acquired while theuser is in motion, FFTs may be performed on the signal and spectralpeaks extracted, which may then be subsequently processed by amultiple-target tracker which starts, continues, merges, and deletestracks of the spectra. In some embodiments, a similar set of operationsmay be performed on the motion signal and the output may be used to doactivity discrimination (e.g., sedentary, walking, running, sleeping,lying down, sitting, biking, typing, elliptical, weight training) whichis used to assist the multiple-target tracker. For instance, it may bedetermined that the user was stationary and has begun to move. Thisinformation may be used to preferentially bias the track continuationtoward increasing frequencies. Similarly, the activity discriminator maydetermine that the user has stopped running or is running slower andthis information may be used to preferentially bias the trackcontinuation toward decreasing frequencies. Tracking may be achievedwith single-scan or multi-scan, multiple-target tracker topologies suchas joint probabilistic data association trackers, multiple-hypothesistracking, nearest neighbor, etc. Estimation and prediction in thetracker may be done through Kalman filters, spline regression, particlefilters, interacting multiple model filters, etc. A track selectormodule may use the output tracks from the multiple-spectra tracker andestimate the user's heart rate. The estimate may be taken as the maximumlikelihood track, a weight sum of the tracks against their probabilitiesof being the heart rate, etc. The activity discriminator may furthermoreinfluence the selection and/or fusion to get the heart rate estimate.For instance, if the user is sleeping, sitting, lying down, orsedentary, a prior probability may be skewed toward heart rates in the40-80 bpm range; whereas if the user is running, jogging, or doing othervigorous exercise, a prior probability may be skewed toward elevatedheart rates in the 90-180 bpm range. The influence of the activitydiscriminator may be based on the speed of the user. The estimate may beshifted toward (or wholly obtained by) the fundamental frequency of thesignal when the user is not moving. The track that corresponds to theuser's heart rate may be selected based on criteria that are indicativeof changes in activity; for instance, if the user begins to walk frombeing stationary, the track that illustrates a shift toward higherfrequency may be preferentially chosen.

The acquisition of a good heart rate signal may be indicated to the userthrough a display on the biometric monitoring device or another devicein wired or wireless communication with the biometric monitoring device(e.g., a Bluetooth Low Energy-equipped mobile phone). In someembodiments, the biometric monitoring device may include asignal-strength indicator that is represented by the pulsing of an LEDviewable by the user. The pulsing may be timed or correlated to becoincident with the user's heartbeat. The intensity, pulsing rate and/orcolor of the LED may be modified or adjusted to suggest signal strength.For example, a brighter LED intensity may represent a stronger signal orin an RGB LED configuration, a green colored LED may represent astronger signal.

In some embodiments, the strength of the heart rate signal may bedetermined by the energy (e.g., squared sum) of the signal in afrequency band of, for instance, 0.5 Hz to 4 Hz. In other embodiments,the biometric monitoring device may have a strain gauge, pressuresensor, force sensor, or other contact-indicating sensor that may beincorporated or constructed into the housing and/or in the band (inthose embodiments where the biometric monitoring device is attached toor mounted with a band like a watch, bracelet, and/or armband—which maythen be secured to the user). A signal quality metric (e.g. heart ratesignal quality) may be calculated based on data from these contactsensors either alone or in combination with data from the heart ratesignal.

In another embodiment, the biometric monitoring device may monitor heartrate optically through an array of photodetectors such as a grid ofphotodiodes or a CCD camera. Motion of the optical device with respectto the skin may be tracked through feature-tracking of the skin and/oradaptive motion correction using an accelerometer and gyroscope. Thedetector array may be in contact with the skin or offset at a smalldistance away from the skin. The detector array and its associatedoptics may be actively controlled (e.g., with a motor) to maintain astabilized image of the target and acquire a heart rate signal. Thisoptomechanical stabilization may be achieved using information frommotion sensors (e.g., a gyroscope) or image features. In one embodiment,the biometric monitoring device may implement relative motioncancellation using a coherent or incoherent light source to illuminatethe skin and a photodetector array with each photodetector associatedwith comparators for comparing the intensity between neighboringdetectors—obtaining a so-called speckle pattern which may be trackedusing a variety of image tracking techniques such as optical flow,template matching, edge tracking, etc. In this embodiment, the lightsource used for motion tracking may be different than the light sourceused in the optical pulse waveform monitor or heart rate monitor.

In another embodiment, the biometric monitoring device may consist of aplurality of photodetectors and photoemitters distributed along asurface of the device that touches the user's skin (i.e., the skin-sideof the biometric monitoring device). (See, for example, FIGS. 9A through13A). In the example of a bracelet, for instance, there may be aplurality of photodetectors and photoemitters placed at various sitesalong the circumference of the interior of the band. (See, for example,FIG. 13A). A heart rate signal-quality metric associated with each sitemay be calculated to determine the best or set of best sites forestimating the user's heart rate. Subsequently, some of the sites may bedisabled or turned off to, for example, reduce power consumption. Thedevice may periodically check the heart rate signal quality at some orall of the sites to enhance, monitor and/or optimize signal and/or powerefficiency.

In another embodiment, a biometric monitoring device may include a pulsewaveform monitor or heart rate monitoring system including a pluralityof sensors such as optical, acoustic, pressure, electrical (e.g., ECG orEKG), and motion and fuse the information from two or more of thesesensors to provide an estimate of heart rate and/or mitigate noiseinduced from motion.

In addition to pulse waveform monitor or heart rate monitoring (or otherbiometric monitoring), or in lieu thereof, the biometric monitoringdevice, in some embodiments, may include optical sensors to track ordetect time and duration of ultraviolet light exposure, total outdoorlight exposure, the type of light source and duration and intensity ofthat light source (fluorescent light exposure, incandescent bulb lightexposure, halogen, etc.), exposure to television (based on light typeand flicker rate), whether the user is indoors or outdoors, time of dayand location based on light conditions. In one embodiment, theultraviolet detection sensor may consist of a reverse biased LED emitterdriven as a light detector. The photocurrent produced by this detectormay be characterized by, for instance, measuring the time it takes forthe LED's capacitance (or alternately a parallel capacitor) todischarge.

All of the optical sensors discussed herein may be used in conjunctionwith other sensors to improve detection of the data described above orbe used to augment detection of other types of physiological orenvironmental data.

Where the biometric monitoring device includes an audio or passiveacoustic sensor, the device may contain one or more passive acousticsensors that detect sound and pressure and that can include, but are notlimited to, microphones, piezo films, etc. The acoustic sensors may bedisposed on one or more sides of the device, including the side thattouches or faces the skin (skin-side) and the sides that face theenvironment (environmental sides).

Skin-side acoustic or audio sensors may detect any type of soundtransmitted through the body and such sensors may be arranged in anarray or pattern that optimizes both the signal-to-noise-ratio and powerconsumption of such sensors. These sensors may detect respiration (e.g.,by listening to the lung), respiratory sounds (e.g., breathing, snoring)and problems (e.g., sleep apnea, etc.), heart rate (listening to theheart beat), user's voice (via sound transmitted from the vocal cordsthroughout the body).

The biometric monitoring devices of the present disclosure may alsoinclude galvanic skin-response (GSR) circuitry to measure the responseof the user's skin to emotional and physical stimuli or physiologicalchanges (e.g., the transition of sleep stage). In some embodiments, thebiometric monitoring device may be a wrist- or arm-mounted deviceincorporating a band made of conductive rubber or fabric so that thegalvanic skin response electrodes may be hidden in the band. Because thegalvanic skin response circuitry may be subjected to changingtemperatures and environmental conditions, it may also include circuitryto enable automatic calibration, such as two or more switchablereference resistors in parallel or in series with the humanskin/electrode path that allows real-time measurement of known resistorsto characterize the response of the galvanic skin response circuit. Thereference resistors may be switched into and out of the measurement pathsuch that they are measured independently and/or simultaneously with theresistance of the human skin.

Circuits for performing PPG

PPG circuitry may be optimized to obtain the best quality signalregardless of a variety of environmental conditions including, but notlimited to, motion, ambient light, and skin color. The followingcircuits and techniques may be used to perform such optimization (seeFigures #PPA through #PPJ);

-   -   a sample-and-hold circuit and differential/instrumentation        amplifier which may be used in PPG sensing. The output signal is        an amplified difference between current and previous sample,        referenced to a given voltage.    -   controlled current source to offset “bias” current prior to        transimpedance amplifier. This allows greater gain to be applied        at transimpedance amplifier stage.    -   a sample-and-hold circuit for current feedback applied to        photodiode (prior to transimpedance amplifier). This can be used        for ambient light removal, or “bias” current removal, or as a        pseudo differential amplifier (may require dual rails).    -   a differential/instrumentation amplifier with ambient light        cancellation.    -   a photodiode offset current generated dynamically by a DAC.    -   a photodiode offset current generated dynamically by controlled        voltage source.    -   ambient light removal using a “switched capacitor” method.    -   photodiode offset current generated by a constant current source        (also can be done with a constant voltage source and a        resistor).    -   ambient light removal and differencing between consecutive        samples.    -   ambient light removal and differencing between consecutive        samples.

Figure #PPA illustrates an example schematic of a sample-and-holdcircuit and differential/instrumentation amplifier which may be used inPPG sensing. The output signal in such a circuit may be an amplifieddifference between a current sample and a previous sample, referenced toa given voltage.

Figure #PPB illustrates an example schematic of a circuit for a PPGsensor using a controlled current source to offset “bias” current priorto a transimpedance amplifier. This allows greater gain to be applied atthe transimpedance amplifier stage.

Figure #PPC illustrates an example schematic of a circuit for a PPGsensor using a sample-and-hold circuit for current feedback applied tophotodiode (prior to a transimpedance amplifier). This circuit may beused for ambient light removal, or “bias” current removal, or as apseudo-differential amplifier.

Figure #PPD illustrates an example schematic of a circuit for a PPGsensor using a differential/instrumentation amplifier with ambient lightcancellation functionality.

Figure #PPE illustrates an example schematic of a circuit for a PPGsensor using a photodiode offset current generated dynamically by a DAC.

Figure #PPF illustrates an example schematic of a circuit for a PPGsensor using a photodiode offset current generated dynamically by acontrolled voltage source.

Figure #PPG illustrates an example schematic of a circuit for a PPGsensor including ambient light removal functionality using a “switchedcapacitor” method.

Figure #PPH illustrates an example schematic of a circuit for a PPGsensor that uses a photodiode offset current generated by a constantcurrent source (this may also be done using a constant voltage sourceand a resistor).

Figure #PPI illustrates an example schematic of a circuit for a PPGsensor that includes ambient light removal functionality anddifferencing between consecutive samples.

Figure #PPJ illustrates an example schematic of a circuit for ambientlight removal and differencing between consecutive samples.

Various circuits and concepts related to heart rate measurement using aPPG sensor are discussed in more detail in U.S. Provisional PatentApplication No. 61/946,439, filed Feb. 28, 2014, which was previouslyincorporated herein by reference in the “Cross-Reference to RelatedApplications” section and which is again hereby incorporated byreference with respect to content directed at heart rate measurementswith a PPG sensor and at circuits, methods, and systems for performingsuch measurements, e.g., to compensate for sensor saturation, ambientlight, and skin tone.

Biometric Feedback

Some embodiments of biometric monitoring devices may provide feedback tothe user based on one or more biometric signals. In one embodiment, aPPG signal may be presented to the user as a real-time or near-real-timewaveform on a display of the biometric monitoring device (or on adisplay of a secondary device in communication with the biometricmonitoring device). This waveform may provide similar feedback to thewaveform displayed on an ECG or EKG machine. In addition to providingthe user with an indication of the PPG signal which may be used toestimate various heart metrics (e.g., heart rate), the waveform may alsoprovide feedback that may enable the user to optimize the position andpressure with which they are wearing the biometric monitoring device.For example, the user may see that the waveform has a low amplitude. Inresponse to this, the user may try moving the position of the biometricmonitoring device to a different location which gives a higher amplitudesignal. In some implementations, the biometric monitoring device may,based on such indications, provide instructions to the user to move oradjust the fit of the biometric monitoring device so as to improve thesignal quality.

In another embodiment, feedback about the quality of the PPG signal maybe provided to the user through a method other than displaying thewaveform. The biometric monitoring device may emit an auditory alarm(e.g. a beep) if the signal quality (e.g. signal to noise ratio) exceedsa certain threshold. The biometric monitoring device may provide avisual cue (through the use of a display for example) to the user toeither change the position of the sensor and/or increase the pressurewith which the device is being worn (for example by tightening a wriststrap in the case that the device is worn on the wrist).

Biometric feedback may be provided for sensors other than PPG sensors.For example, if the device uses ECG, EMG, or is connected to a devicewhich performs either of these, it may provide feedback to the userregarding the waveform from those sensors. If the signal-to-noise-ratioof these sensors is low, or the signal quality is otherwise compromised,the user may be instructed on how they can improve the signal. Forexample, if the heart rate cannot be detected from the ECG sensor, thedevice may provide a visual message to the user instructing them to wetor moisten the ECG electrodes to improve the signal.

Environmental Sensors

Some embodiments of biometric monitoring devices of the presentdisclosure may use one, some or all of the following environmentalsensors to, for example, acquire the environmental data, includingenvironmental data outlined in the table below. Such biometricmonitoring devices are not limited to the number or types of sensorsspecified below but may employ other sensors that acquire environmentaldata outlined in the table below. All combinations and permutations ofenvironmental sensors and/or environmental data are intended to fallwithin the scope of the present disclosure. Additionally, the device mayderive environmental data from the corresponding sensor output data, butis not limited to the types of environmental data that it could derivefrom said sensor.

Notably, embodiments of biometric monitoring devices of the presentdisclosure may use one or more, or all of the environmental sensorsdescribed herein and one or more, or all of the physiological sensorsdescribed herein. Indeed, biometric monitoring device of the presentdisclosure may acquire any or all of the environmental data andphysiological data described herein using any sensor now known or laterdeveloped—all of which are intended to fall within the scope of thepresent disclosure.

Environmental Sensors Environmental data acquired Motion DetectorLocation Potential Embodiments: Inertial, Gyroscopic orAccelerometer-based Sensors GPS Pressure/Altimeter sensor ElevationAmbient Temp Temperature Light Sensor Indoor vs outdoor Watching TV(spectrum/flicker rate detection) Optical data transfer-initiation, QRcodes, etc. Ultraviolet light exposure Audio Indoor vs. Outdoor CompassLocation and/or orientation Potential Embodiments: 3 Axis Compass

In one embodiment, the biometric monitoring device may include analtimeter sensor, for example, disposed or located in the interior ofthe device housing. (See, for example, FIGS. 19B and 19C; FIG. 19Cillustrates an example of a portable biometric monitoring device havingphysiological sensors, environmental sensors, and location sensorsconnected to a processor). In such a case, the device housing may have avent that allows the interior of the device to measure, detect, sampleand/or experience any changes in exterior pressure. In one embodiment,the vent may prevent water from entering the device while facilitatingmeasuring, detecting and/or sampling changes in pressure via thealtimeter sensor. For example, an exterior surface of the biometricmonitoring device may include a vent type configuration or architecture(for example, a Gore™ vent) that allows ambient air to move in and outof the housing of the device (which allows the altimeter sensor tomeasure, detect and/or sample changes in pressure), but reduces,prevents, and/or minimizes water and other liquids from flowing into thehousing of the device.

The altimeter sensor, in one embodiment, may be filled with gel thatallows the sensor to experience pressure changes outside of the gel. Thegel may act as a relatively impervious, incompressible, yet flexible,membrane that transmits external pressure variations to the altimeterwhile physically separating the altimeter (and other internalcomponents) from the outside environment. The use of a gel-filledaltimeter may give the device a higher level of environmental protectionwith or without the use of an environmentally sealed vent. The devicemay have a higher survivability rate with a gel-filled altimeter inlocations including, but not limited to, locations that have highhumidity, clothes washers, dish washers, clothes dryers, a steam room orsauna, a shower, a pool, a bath, and any location where the device maybe exposed to moisture, exposed to liquid, or submerged in liquid.

Sensors Integration/Signal Processing

Some embodiments of the biometric monitoring devices of the presentdisclosure may use data from two or more sensors to calculate thecorresponding physiological or environmental data as seen in the tablebelow (for example, data from two or more sensors may be used incombination to determine metrics such as those listed below). Thebiometric monitoring device may include, but is not limited to, thenumber, types, or combinations of sensors specified below. Additionally,such biometric monitoring devices may derive the included data from thecorresponding sensor combinations, but are not limited to the number ortypes of data that may be calculated from the corresponding sensorcombinations.

Data derived from signal processing of Sensor Integrations multiplesensors Skin Temp and Ambient Temp Heat Flux Heart Rate and MotionElevation gain Motion detector and other user's motion Users in theproximity detector (linked by wireless communication path) Motion, anypulse waveform sensor, Sit/Standing detection galvanic skin response Anyheart rate, heart rate variability Sleep Phase detection sensor,respiration, motion Sleep Apnea detection Any pulse waveform sensorand/or Resting Heart rate wetness sensor, and/or motion detector ActiveHeart Rate Heart rate while asleep Heart rate while sedentary Any heartrate detector Early detection of heart problems: Cardiac ArrhythmiaCardiac Arrest Multiple heart rate detectors Pulse transit time Audioand/or strain gauge Typing detection GPS and photoplethysmography (PPG)Location-stress correlation: determination of stressful regionsdetermination of low stress regions Activity-specific heart rate restingheart rate active heart rate Automatic activity classification andactivity heart rate determination Heart rate, galvanic skin response,User fatigue, for example while exercising accelerometer and respiration

In some embodiments, the biometric monitoring device may also include anear-field communication (NFC) receiver/transmitter to detect proximityto another device, such as a mobile phone. When the biometric monitoringdevice is brought into close or detectable proximity to the seconddevice, it may trigger the start of new functionality on the seconddevice (e.g., the launching of an “app” on the mobile phone and radiosyncing of physiological data from the device to the second device).(See, for example, FIG. 17). Indeed, the biometric monitoring device ofthe present disclosure may implement any of the circuitry and techniquesdescribed and/or illustrated in U.S. Provisional Patent Application61/606,559, filed Mar. 5, 2012, “Near Field Communication System, andMethod of Operating Same”, inventor: James Park (the contents of whichare incorporated herein by reference for such purpose).

FIG. 17 illustrates an example of a portable biometric monitoring devicethat has a bicycle application on it that may display bicycle speedand/or pedaling cadence, among other metrics. The app may be activatedwhenever the biometric monitoring device comes into proximity of apassive or active NFC tag. This NFC tag may be attached to the user'shandlebars.

In another embodiment, the biometric monitoring device may include alocation sensor (for example, GPS circuitry) and pulse waveform sensor(for example, photoplethysmography circuitry) to generate GPS- orlocation-related data and heart rate-related data, respectively. (See,for example, FIGS. 19B and 19C). The biometric monitoring device maythen fuse, process and/or combine data from these twosensors/circuitries to, for example, determine, correlate, and/or “map”geographical regions according to physiological data (for example, heartrate, stress, activity level, quantity of sleep and/or caloric intake).In this way, the biometric monitoring device may identify geographicalregions that increase or decrease a measurable user metric including,but not limited to, heart rate, stress, activity, level, quantity ofsleep and/or caloric intake.

In addition thereto, or in lieu thereof, some embodiments of biometricmonitoring devices may employ GPS-related data andphotoplethysmography-related data (notably, each of which may beconsidered data streams) to determine or correlate the user's heart rateaccording to activity levels—for example, as determined by the user'sacceleration, speed, location and/or distance traveled (as measured bythe GPS and/or determined from GPS-related data). (See, for example,FIGS. 19B and 19C). Here, in one embodiment, heart rate as a function ofspeed may be “plotted” for the user, or the data may be broken down intodifferent levels including, but not limited to, sleeping, resting,sedentary, moderately active, active, and highly active.

Indeed, some embodiments of biometric monitoring devices may alsocorrelate GPS-related data to a database of predetermined geographiclocations that have activities associated with them for a set ofpredetermined conditions. For example, activity determination andcorresponding physiological classification (for example, heart rateclassification) may include correlating a user's GPS coordinates thatcorrespond to location(s) of exercise equipment, health club and/or gymand physiological data. Under these circumstances, a user's heart rateduring, for example a gym workout, may be automatically measured anddisplayed. Notably, many physiological classifications may be based onGPS-related data including location, acceleration, altitude, distanceand/or velocity. Such a database including geographic data andphysiological data may be compiled, developed and/or stored on thebiometric monitoring device and/or external computing device. Indeed, inone embodiment, the user may create their own location database or addto or modify the location database to better classify their activities.

In another embodiment, the user may simultaneously wear multiplebiometric monitoring devices (having any of the features describedherein). The biometric monitoring devices of this embodiment maycommunicate with each other or a remote device using wired or wirelesscircuitry to calculate, for example, biometric or physiologic qualitiesor quantities that, for example, may be difficult or inaccurate tocalculate otherwise, such as pulse transit time. The use of multiplesensors may also improve the accuracy and/or precision of biometricmeasurements over the accuracy and/or precision of a single sensor. Forexample, having a biometric tracking device on the waist, wrist, andankle may improve the detection of the user taking a step over that of asingle device in only one of those locations. Signal processing may beperformed on the biometric tracking devices in a distributed orcentralized method to provide measurements improved over that of asingle device. This signal processing may also be performed remotely andcommunicated back to the biometric tracking devices after processing.

In another embodiment, heart rate or other biometric data may becorrelated to a user's food log (a log of foods ingested by a user,their nutritional content, and portions thereof). Food log entries maybe entered into the food log automatically or may be entered by the userthemselves through interaction with the biometric monitoring device (ora secondary or remote device, e.g., a smartphone, in communication withthe biometric monitoring device or some other device, e.g., a server, incommunication with the biometric monitoring device). Information may bepresented to the user regarding the biometric reaction of their body toone or more food inputs. For example, if a user has coffee, their heartrate may rise as a result of the caffeine. In another example, if a userhas a larger portion of food late at night, it may take longer for themto fall asleep than usual. Any combination of food input andcorresponding result in biometrics may be incorporated into such afeedback system.

The fusion of food intake data and biometric data may also enable someembodiments of biometric monitoring device to make an estimation of auser's glucose level. This may be particularly useful for users who havediabetes. With an algorithm which relates the glucose level to theuser's activity (e.g. walking, running, calorie burn) and nutritionalintake, a biometric monitoring device may be able to advise the userwhen they are likely to have an abnormal blood sugar level.

Processing Task Delegation

Embodiments of biometric monitoring devices may include one or moreprocessors. Figures. For example, an independent application processormay be used to store and execute applications that utilize sensor dataacquired and processed by one or more sensor processors (processor(s)that process data from physiological, environmental, and/or activitysensors). In the case where there are multiple sensors, there may alsobe multiple sensor processors. An application processor may have sensorsdirectly connected to it as well. Sensor and application processors mayexist as separate discrete chips or exist within the same packaged chip(multi-core). A device may have a single application processor, or anapplication processor and sensor processor, or a plurality ofapplication processors and sensor processors.

In one embodiment, the sensor processor may be placed on a daughterboardthat consists of all of the analog components. This board may have someof the electronics typically found on the main PCB such as, but notlimited to, transimpedance amplifiers, filtering circuits, levelshifters, sample-and-hold circuits, and a microcontroller unit. Such aconfiguration may allow the daughterboard to be connected to the mainPCB through the use of a digital connection rather than an analogconnection (in addition to any necessary power or ground connections). Adigital connection may have a variety of advantages over an analogdaughterboard to main PCB connection, including, but not limited to, areduction in noise and a reduction in the number of necessary cables.The daughterboard may be connected to the main board through the use ofa flex cable or set of wires.

Multiple applications may be stored on an application processor. Anapplication may consist of executable code and data for the application,but is not limited to these. Data may consist of graphics or otherinformation required to execute the application or it may be informationoutput generated by the application. The executable code and data forthe application may both reside on the application processor (or memoryincorporated therein) or the data for the application may be stored andretrieved from an external memory. External memory may include but isnot limited to NAND flash, NOR flash, flash on another processor, othersolid-state storage, mechanical or optical disks, RAM, etc.

The executable code for an application may also be stored in an externalmemory. When a request to execute an application is received by theapplication processor, the application processor may retrieve theexecutable code and/or data from the external storage and execute it.The executable code may be temporarily or permanently stored on thememory or storage of the application processor. This allows theapplication to be executed more quickly on the next execution request,since the step of retrieval is eliminated. When the application isrequested to be executed, the application processor may retrieve all ofthe executable code of the application or portions of the executablecode. In the latter case, only the portion of executable code requiredat that moment is retrieved. This allows applications that are largerthan the application processor's memory or storage to be executed.

The application processor may also have memory protection features toprevent applications from overwriting, corrupting, interrupting,blocking, or otherwise interfering with other applications, the sensorsystem, the application processor, or other components of the system.

Applications may be loaded onto the application processor and/or anyexternal storage via a variety of wired, wireless, optical, orcapacitive mechanisms including, but not limited to, USB, Wi-Fi,Bluetooth, Bluetooth Low Energy, NFC, RFID, Zigbee.

Applications may also be cryptographically signed with an electronicsignature. The application processor may restrict the execution ofapplications to those that have the correct signature.

Integration of Systems in a Biometric Monitoring Device

In some implementations of biometric monitoring devices, some sensors orelectronic systems in the biometric monitoring device may be integratedwith one another or may share components or resources. For example, aphotodetector for an optically-based pulse waveform sensor (such as maybe used in the heart-rate sensors discussed in U.S. Provisional PatentApplication No. 61/946,439, filed Feb. 28, 2014, and previouslyincorporated by reference herein, may also serve as a photodetector fordetermining ambient light level, such as may be used to correct for theeffects of ambient light on the pulse waveform sensor reading. Forexample, if the light source for such a heart rate detector is turnedoff, the light that is measured by the photodetector may be indicativeof the amount of ambient light that is present.

In some implementations of a biometric monitoring device, the biometricmonitoring device may be configured or communicated with using onboardoptical sensors such as the components in an optical pulse waveformmonitor or heart rate monitor. For example, the photodetectors of anoptical heart-rate sensor (or, if present, an ambient light sensor) mayalso serve as a receiver for an optically-based transmission channel,e.g., infrared communications.

In some implementations of a biometric monitoring device, a hybridantenna may be included that combines a radio frequency antenna, e.g., aBluetooth antenna or GPS antenna, with an inductive loop, such as may beused in a near-field communications (NFC) tag or in an inductivecharging system. In such implementations, the functionality for twodifferent systems may be provided in one integrated system, savingpacking volume. In such a hybrid antenna, an inductive loop may beplaced in close proximity to the radiator of an inverted-F antenna. Theinductive loop may inductively couple with the radiator, allowing theinductive loop to serve as a planar element of the antenna forradio-frequency purposes, thus forming, for example, a planar inverted-Fantenna. At the same time, the inductive loop may also serve its normalfunction, e.g., such as providing current to an NFC chip throughinductive coupling with an electromagnetic field generated by an NFCreader. Examples of such hybrid antenna systems are discussed in moredetail in U.S. Provisional Patent Application No. 61/948,470, filed Mar.5, 2014, which was previously incorporated herein by reference in the“Cross-Reference to Related Applications” section and which is againhereby incorporated by reference with respect to content directed athybrid antenna structures. Of course, such hybrid antennas may also beused in other electronic devices other than biometric monitoringdevices, and such non-biometric-monitoring-device use of hybrid antennasis contemplated as being within the scope of this disclosure.

User Interface with the Device

Some embodiments of a biometric monitoring device may includefunctionality for allowing one or more methods of interacting with thedevice either locally or remotely.

In some embodiments, the biometric monitoring device may convey datavisually through a digital display. The physical embodiment of thisdisplay may use any one or a plurality of display technologiesincluding, but not limited to one or more of LED, LCD, AMOLED, E-Ink,Sharp display technology, graphical displays, and other displaytechnologies such as TN, HTN, STN, FSTN, TFT, IPS, and OLET. Thisdisplay may show data acquired or stored locally on the device or maydisplay data acquired remotely from other devices or Internet services.The biometric monitoring device may use a sensor (for example, anAmbient Light Sensor, “ALS”) to control or adjust the amount of screenbacklighting, if backlighting is used. For example, in dark lightingsituations, the display may be dimmed to conserve battery life, whereasin bright lighting situations, the display brightness may be increasedso that it is more easily read by the user.

In another embodiment, the biometric monitoring device may use single ormulticolor LEDs to indicate a state of the device. States that thebiometric monitoring device may indicate using LEDs may include, but arenot limited to, biometric states such as heart rate or applicationstates such as an incoming message or that a goal has been reached.These states may be indicated through the LED's color, the LED being onor off (or in an intermediate intensity), pulsing (and/or rate thereof)of the LEDs, and/or a pattern of light intensities from completely offto highest brightness. In one embodiment, an LED may modulate itsintensity and/or color with the phase and frequency of the user's heartrate.

In some embodiments, the use of an E-Ink display may allow the displayto remain on without the battery drain of a non-reflective display. This“always-on” functionality may provide a pleasant user experience in thecase of, for example, a watch application where the user may simplyglance at the biometric monitoring device to see the time. The E-Inkdisplay always displays content without compromising the battery life ofthe device, allowing the user to see the time as they would on atraditional watch.

Some implementations of a biometric monitoring device may use a lightsuch as an LED to display the heart rate of the user by modulating theamplitude of the light emitted at the frequency of the user's heartrate. The device may depict heart rate zones (e.g., aerobic, anaerobic,etc.) through the color of an LED (e.g., green, red) or a sequence ofLEDs that light up in accordance with changes in heart rate (e.g., aprogress bar). The biometric monitoring device may be integrated orincorporated into another device or structure, for example, glasses orgoggles, or communicate with glasses or goggles to display thisinformation to the user.

Some embodiments of a biometric monitoring device may also conveyinformation to a user through the physical motion of the device. Onesuch embodiment of a method to physically move the device is the use ofa vibration-inducing motor. The device may use this method alone, or incombination with a plurality of other motion-inducing technologies.

In some implementations, a biometric monitoring device may conveyinformation to a user through audio feedback. For example, a speaker inthe biometric monitoring device may convey information through the useof audio tones, voice, songs, or other sounds.

These three information communication methods—visual, motion, andauditory—may, in various embodiments of biometric monitoring devices, beused alone or in any combination with each other or another method ofcommunication to communicate any one or plurality of the followinginformation:

-   -   That a user needs to wake up at certain time    -   That a user should wake up as they are in a certain sleep phase    -   That a user should go to sleep as it is a certain time    -   That a user should wake up as they are in a certain sleep phase        and in a preselected time window bounded by the earliest and        latest time that the user wants to wake up.    -   That an email was received    -   That the user has been inactive for a certain period of time.        Notably, this may integrate with other applications like, for        instance, a meeting calendar or sleep tracking application to        block out, reduce, or adjust the behavior of the inactivity        alert.    -   That the user has been active for a certain period of time    -   That the user has an appointment or calendar event    -   That the user has reached a certain activity metric    -   That the user has gone a certain distance    -   That the user has reached a certain mile pace    -   That the user has reached a certain speed    -   That the user has accumulated a certain elevation gain    -   That the user has taken a certain number of steps    -   That the user has had a pulse waveform measurement or heart rate        measurement recently    -   That the user's heart rate has reached a certain level    -   That the user has a normal, active, or resting heart rate of a        specific value or in a specific range    -   That the user's heart rate has enter or exited a certain goal        range or training zone    -   That the user has a new heart rate “zone” goal to reach, as in        the case of heart rate zone training for running, bicycling,        swimming, etc. activities    -   That the user has swum a lap or completed a certain number of        laps in a pool    -   An external device has information that needs to be communicated        to the user such as an incoming phone call or any one of the        above alerts    -   That the user has reached a certain fatigue goal or limit. In        one embodiment, fatigue may be determined through a combination        of heart rate, galvanic skin response, motion sensor, and/or        respiration data

These examples are provided for illustration and are not intended tolimit the scope of information that may be communicated by suchembodiments of biometric monitoring devices (for example, to the user).Note that the data used to determine whether or not an alert conditionis met may be acquired from a first device and/or one or more secondarydevices. The biometric monitoring device itself may determine whetherthe criteria or conditions for an alert have been met. Alternatively, acomputing device in communication with the biometric monitoring device(e.g., a server and/or a mobile phone) may determine when the alertshould occur. In view of this disclosure, other information that thebiometric monitoring device may communicate to the user may beenvisioned by one of ordinary skill in the art. For example, thebiometric monitoring device may communicate with the user when a goalhas been met. The criteria for meeting this goal may be based onphysiological, contextual, and environmental sensors on a first device,and/or other sensor data from one or more secondary devices. The goalmay be set by the user or may be set by the biometric monitoring deviceitself and/or another computing device in communication with thebiometric monitoring device (e.g. a server). In an example embodiment,the biometric monitoring device may vibrate when a biometric goal ismet.

Some embodiments of biometric monitoring devices of the presentdisclosure may be equipped with wireless and/or wired communicationcircuitry to display data on a secondary device in real time. Forexample, such biometric monitoring devices may be able to communicatewith a mobile phone via Bluetooth Low Energy in order to give real-timefeedback of heart rate, heart rate variability, and/or stress to theuser. Such biometric monitoring devices may coach or grant “points” forthe user to breathe in specific ways that alleviate stress (e.g. bytaking slow, deep breaths). Stress may be quantified or evaluatedthrough heart rate, heart rate variability, skin temperature, changes inmotion-activity data and/or galvanic skin response.

Some embodiments of biometric monitoring devices may receive input fromthe user through one or more local or remote input methods. One suchembodiment of local user input may use a sensor or set of sensors totranslate a user's movement into a command to the device. Such motionscould include but may not be limited to tapping, rolling the wrist,flexing one or more muscles, and swinging one's arm. Another user inputmethod may be through the use of a button such as, but not limited to,capacitive touch buttons, capacitive screen buttons, and mechanicalbuttons. In one embodiment, the user interface buttons may be made ofmetal. In embodiments where the screen uses capacitive touch detection,it may always be sampling and ready to respond to any gesture or inputwithout an intervening event such as pushing a physical button. Suchbiometric monitoring devices may also take input through the use ofaudio commands. All of these input methods may be integrated intobiometric monitoring devices locally or integrated into a remote devicethat can communicate with such biometric monitoring devices, eitherthrough a wired or wireless connection. In addition, the user may alsobe able to manipulate the biometric monitoring device through a remotedevice. In one embodiment, this remote device may have Internetconnectivity.

Alarms

In some embodiments, the biometric monitoring device of the presentdisclosure may act as a wrist-mounted vibrating alarm to silently wakethe user from sleep. Such biometric monitoring devices may track theuser's sleep quality, waking periods, sleep latency, sleep efficiency,sleep stages (e.g., deep sleep vs REM), and/or other sleep-relatedmetrics through one or a combination of heart rate, heart ratevariability, galvanic skin response, motion sensing (e.g.,accelerometer, gyroscope, magnetometer), and skin temperature. The usermay specify a desired alarm time or window of time (e.g., set alarm togo off between 7 am and 8 am). Such embodiments may use one or more ofthe sleep metrics to determine an optimal time within the alarm windowto wake the user. In one embodiment, when the vibrating alarm is active,the user may cause it to hibernate or turn off by slapping or tappingthe device (which is detected, for example, via motion sensor(s), apressure/force sensor, and/or capacitive touch sensor in the device). Inone embodiment, the device may attempt to arouse the user at an optimumpoint in the sleep cycle by starting a small vibration at a specificuser sleep stage or time prior to the alarm setting. It mayprogressively increase the intensity or noticeability of the vibrationas the user progresses toward wakefulness or toward the alarm setting.(See, for example, FIG. 15).

FIG. 15 illustrates functionality of an example portable biometricmonitoring device smart alarm feature. The biometric monitoring devicemay be able to detect or may be in communication with a device that candetect the sleep stage or state of a user (e.g., light or deep sleep).The user may set a window of time which they would like to be awoken(e.g., 6:15 am to 6:45 am). The smart alarm may be triggered by the usergoing into a light sleep state during the alarm window.

The biometric monitoring device may be configured to allow the user toselect or create an alarm vibration pattern of their choice. The usermay have the ability to “snooze” or postpone an alarm event. In oneembodiment, the user may be able to set the amount of delay for the“snooze” feature—the delay being the amount of time before the alarmwill go off again. They may also be able to set how many times thesnooze feature may be activated per alarm cycle. For example, a user maychoose a snooze delay of 5 minutes and a maximum sequential snoozenumber to be 3. Therefore, they can press snooze up to 3 times to delaythe alarm by 5 minutes each time they press snooze to delay the alarm.In such embodiments, the snooze function will not turn off the alarm ifthe user attempts to press snooze a fourth time.

Some biometric monitoring devices may have information about the user'scalendar and/or schedule. The user's calendar information may be entereddirectly into the biometric monitoring device or it may be downloadedfrom a different device (e.g. a smartphone). This information may beused to automatically set alarms or alarm characteristics. For example,if a user has a meeting at 9 am in the morning, the biometric monitoringdevice may automatically wake the user up at 7:30 am to allow the userenough time to prepare for and/or get to the meeting. The biometricmonitoring device may determine the amount of time required for the userto prepare for the meeting based on the user's current location, thelocation of the meeting, and the amount of time it would take to get thelocation of the meeting from the user's current location. Alternatively,historical data about how long the user takes to get to the meetinglocation and/or prepare to leave for the meeting (e.g. how long it takesto wake up, take a shower, have breakfast, etc. in the morning) may beused to determine at what time to wake the user. A similar functionalitymay be used for calendar events other than meetings such as eatingtimes, sleeping times, napping times, and exercise times.

In some embodiments, the biometric monitoring device may use informationon when the user went to sleep to determine when an alarm should go offto wake the user. This information may supplement calendar informationdescribed herein. The user may have a goal of approximately how manyhours of sleep they would like to get each night or week. The biometricmonitoring device may set the morning alarm at the appropriate time forthe user to meet these sleep goals. In addition to amount of time thatthe user would like to sleep each night, other sleep goals that the usermay set may include, but are not limited to, the amount of deep sleep,REM sleep, and light sleep that the user experiences while sleeping, allof which may be used by the biometric monitoring device to determinewhen to set an alarm in the morning. Additionally, the user may bealerted at night when they should go to bed to meet their sleep goals.Additionally, the user may be alerted during the day when they shouldtake a nap to meet their sleep goals. The time at which to alert a userthat they should take a nap may be determined by factors that optimizethe user's sleep quality during the nap, subsequent naps, or night-timesleep. For example, the user is likely to have a hard time fallingasleep at night if they took a nap in the early evening. The user mayalso be advised to eat certain foods or drinks or avoid certain foods ordrinks to optimize their sleep quality. For example, a user may bediscouraged from drinking alcohol close to their bed time as it islikely to decrease their sleep quality. The user may also be advised toperform certain activities or avoid certain activities to optimize theirsleep quality. For example, a user may be encouraged to exercise in theearly afternoon to improve their sleep quality. A user may bediscouraged from exercising or watching TV close to their bedtime toimprove their sleep quality.

User Interface with a Secondary Device

In some embodiments, the biometric monitoring device may transmit andreceive data and/or commands to and/or from a secondary electronicdevice. The secondary electronic device may be in direct or indirectcommunication with the biometric monitoring device. Direct communicationrefers herein to the transmission of data between a first device and asecondary device without any intermediary devices. For example, twodevices may communicate to one another over a wireless connection (e.g.Bluetooth) or a wired connection (e.g. USB). Indirect communicationrefers to the transmission of data between a first device and asecondary device with the aid of one or multiple intermediary thirddevices which relay the data. Third devices may include, but are notlimited to, a wireless repeater (e.g. WiFi repeater), a computing devicesuch as a smartphone, laptop, desktop or tablet computer, a cell phonetower, a computer server, and other networking electronics. For example,a biometric device may send data to a smartphone which forwards the datathrough a cellular network data connection to a server which isconnected through the internet to the cellular network.

In some embodiments, the secondary device that acts as a user interfaceto the biometric monitoring device may consist of a smartphone. An appon the smart phone may facilitate and/or enable the smartphone to act asa user interface to the biometric monitoring device. The biometricmonitoring device may send biometric and other data to the smartphone inreal-time or with some delay. The smartphone may send a command orcommands to the biometric monitoring device, for example, to instruct itto send biometric and other data to the smartphone in real-time or withsome delay. For example, if the user enters a mode in the app fortracking a run, the smartphone may send a command to the biometricdevice to instruct it to send data in real-time. Therefore, the user cantrack their run on their app as they go along without any delay.

Such a smartphone may have one or multiple apps to enable the user toview data from their biometric device or devices. The app may, bydefault, open to a “dashboard” page when the user launches or opens theapp. On this page, summaries of data totals such as the total number ofsteps, floors climbed miles traveled, calories burned, calories consumedand water consumed may be shown. Other pertinent information such as thelast time the app received data from the biometric monitoring device,metrics regarding the previous night's sleep (e.g. when the user went tosleep, woke up, and how long they slept for), and how many calories theuser can eat in the day to maintain their caloric goals (e.g. a caloriedeficit goal to enable weight loss) may also be shown. The user may beable to choose which of these and other metrics are shown on thedashboard screen. The user may be able to see these and other metrics onthe dashboard for previous days. They may be able to access previousdays by pressing a button or icon on a touchscreen. Alternatively,gestures such as swiping to the left or right may enable the user tonavigate through current and previous metrics.

The smartphone app may also have another page which provides a summaryof the user's activities. Activities may include, but are not limitedto, walking, running, biking, cooking, sitting, working, swimming,working out, weightlifting, commuting, and yoga. Metrics pertinent tothese activities may be presented on this page. For example, a bar graphmay show how the number of steps the user took for different portions ofthe day (e.g. how many steps every 5 minutes or 1 hour). In anotherexample, the amount of time the user spent performing a certain activityand how many calories were burned in this period of time may bedisplayed. Similar to the dashboard page, the app may providenavigational functionality to allow the user to see these and othermetrics for past days. Other time periods such as an hour, minute, week,month or year may also be selected by the user to enable them to viewtrends and metrics of their activities over shorter or larger spans oftime.

The smartphone app may also have an interface to log food that has been,or will be, eaten by the user. This interface may have a keyword searchfeature to allow the user to quickly find the food that they would liketo enter into their log. As an alternative to, or in addition to,searching for foods, users may have the ability to find a food to log bynavigating through a menu or series of menus. For example, a user maychoose the following series ofcategories—breakfast/cereal/healthy/oatmeal to arrive at the food whichthey would like to log (e.g., apple-flavored oatmeal). At any one ofthese menus, the user may be able to perform a keyword search. Forexample, the user may search for “oatmeal” after having selected thecategory “breakfast” to search for the keyword “oatmeal” within thecategory of breakfast foods. After having selected the food that theywould like to log, the user may be able to modify or enter the servingsize and nutritional content. After having logged at least one food, theapp may display a summary of the foods that were logged in a certaintime period (e.g. a day) and the nutritional content of the foods(individual and total calorie content, vitamin content, sugar content,etc.).

The smartphone app may also have a page that displays metrics regardingthe user's body such as the user's weight, body fat percentage, BMI, andwaist size. It may display a graph or graphs showing the trend of one ormultiple of these metrics over a certain period of time (e.g., twoweeks). The user may be able to choose the value of this period of timeand view previous time periods (e.g., last month).

The smartphone app may also a page which allows the user to enter howmuch water the user has consumed. Each time the user drinks some water,they may enter that amount in the unit of their choice (e.g., ozs.,cups, etc.). The app may display the total of all of the water the userhas logged within a certain time period (e.g., a day). The app may allowthe user to see previously-logged water entries and daily totals forprevious days as well as the current day.

The smartphone app may also have a page that displays online friends ofthe user. This “friends” page may enable the user to add or request newfriends (e.g., by searching for their name or by their email address).This page may also display a leaderboard of the user and his or herfriends. The user and his or friends may be ranked based on one or moremetrics. For example, the user and his or her friends may be rankedusing the total of the past seven days' step counts.

The smartphone app may also have a page that shows metrics regarding theuser's sleep for the previous night and/or previous nights. This pagemay also enable the user to log when they slept in the past byspecifying when they went to bed and when they woke. The user may alsohave the ability to enter a subjective metric about their sleep (e.g.,bad night's rest, good night's rest, excellent night's rest, etc.). Theuser may be able to view these metrics for days or time periods (e.g.,two weeks) in the past. For example, the sleep page may default toshowing a bar graph of the amount of time the user slept each night inthe last two weeks. The user may be able to also view a bar graph of theamount of time the user slept each night in the last month.

The user may also be able to access the full capabilities of thesmartphone app described herein (e.g., the ability to enter food logs,view dashboard, etc.) through an alternative or additional interface. Inone embodiment, this alternative interface may consist of a webpage thatis hosted by a server in indirect communication with the biometricmonitoring device. The webpage may be accessed through any internetconnected device using a program such as a web browser.

Wireless Connectivity and Data Transmission

Some embodiments of biometric monitoring devices of the presentdisclosure may include a means of wireless communication to transmit andreceive information from the Internet and/or other devices. The wirelesscommunication may consist of one or more interfaces such as Bluetooth,ANT, WLAN, power-line networking, and cell phone networks. These areprovided as examples and should not be understood to exclude otherexisting wireless communication methods or protocols, or wirelesscommunications techniques or protocols that are yet to be invented.

The wireless connection may be bi-directional. The biometric monitoringdevice may transmit, communicate and/or push its data to other devices,e.g., smart phones, computers, etc., and/or the Internet, e.g., webservers and the like. The biometric monitoring device may also receive,request and/or pull data from other devices and/or the Internet.

The biometric monitoring device may act as a relay to providecommunication for other devices to each other or to the Internet. Forexample, the biometric monitoring device may connect to the Internet viaWLAN but also be equipped with an ANT radio. An ANT device maycommunicate with the biometric monitoring device to transmit its data tothe Internet through the biometric monitoring device's WLAN (and viceversa). As another example, the biometric monitoring device may beequipped with Bluetooth. If a Bluetooth-enabled smart phone comes withinrange of the biometric monitoring device, the biometric monitoringdevice may transmit data to, or receive data from, the Internet throughthe smart phone's cell phone network. Data from another device may alsobe transmitted to the biometric monitoring device and stored (or viceversa) or transmitted at a later time.

Embodiments of biometric monitoring devices of the present disclosuremay also include functionality for streaming or transmitting web contentfor display on the biometric monitoring device. The following aretypical examples of such content:

-   -   1. Historical graphs of heart rate and/or other data measured by        the device but stored remotely    -   2. Historical graphs of user activity and/or foods consumed        and/or sleep data that are measured by other devices and/or        stored remotely (e.g., such as at a website like fitbit.com)    -   3. Historical graphs of other user-tracked data that are stored        remotely. Examples include heart rate, blood pressure, arterial        stiffness, blood glucose levels, cholesterol, duration of TV        watching, duration of video game play, mood, etc.    -   4. Coaching and/or dieting data based on one or more of the        user's heart rate, current weight, weight goals, food intake,        activity, sleep, and other data.    -   5. User progress toward heart rate, weight, activity, sleep,        and/or other goals.    -   6. Summary statistics, graphics, badges, and/or metrics (e.g.,        “grades”) to describe the aforementioned data    -   7. Comparisons between the aforementioned data for the user and        similar data for his/her “friends” with similar devices and/or        tracking methods    -   8. Social content such as Twitter feeds, instant messaging,        and/or Facebook updates    -   9. Other online content such as newspaper articles, horoscopes,        weather reports, RSS feeds, comics, crossword puzzles,        classified advertisements, stock reports, and websites    -   10. Email messages and calendar schedules

Content may be delivered to the biometric monitoring device according todifferent contexts. For instance, in the morning, news and weatherreports may be displayed along with the user's sleep data from theprevious night. In the evening, a daily summary of the day's activitiesmay be displayed.

Various embodiments of biometric monitoring devices as disclosed hereinmay also include NFC, RFID, or other short-range wireless communicationcircuitry that may be used to initiate functionality in other devices.For instance, a biometric monitoring device may be equipped with an NFCantenna so that when a user puts it into close proximity with a mobilephone, an app is launched automatically on the mobile phone.

These examples are provided for illustration and are not intended tolimit the scope of data that may be transmitted, received, or displayedby the device, nor any intermediate processing that may occur duringsuch transfer and display. In view of this disclosure/application, manyother examples of data that may be streamed to or via a biometricmonitoring device may be envisioned by one reasonably skilled in theart.

Charging and Data Transmission

Some embodiments of biometric monitoring devices may use a wiredconnection to charge an internal rechargeable battery and/or transferdata to a host device such as a laptop or mobile phone. In oneembodiment, similar to one discussed earlier in this disclosure, thebiometric monitoring device may use magnets to help the user align thebiometric monitoring device to a dock or cable. The magnetic field ofmagnets in the dock or cable and the magnets in the device itself may bestrategically oriented so as to force the biometric monitoring device toself-align with the dock or cable (or, more specifically, a connector onthe cable) and so as to provide a force that holds the biometricmonitoring device in the dock or to the cable. The magnets may also beused as conductive contacts for charging or data transmission purposes.In another embodiment, a permanent magnet may only be used in the dockor cable side and not in the biometric monitoring device itself. Thismay improve the performance of the biometric monitoring device where thebiometric monitoring device employs a magnetometer. If there is a magnetin the biometric monitoring device, the strong field of a nearbypermanent magnet may make it significantly more difficult for themagnetometer to accurately measure the earth's magnetic field. In suchembodiments, the biometric monitoring device may utilize a ferrousmaterial in place of a magnet, and the magnets on the dock or cable sidemay attach to the ferrous material.

In another embodiment, the biometric monitoring device may contain oneor more electromagnets in the biometric monitoring device body. Thecharger or dock for charging and data transmission may also contain anelectromagnet and/or a permanent magnet. The biometric monitoring devicecould only turn on its electromagnet when it is close to the charger ordock. The biometric monitoring device may detect proximity to the dockor charger by looking for the magnetic field signature of a permanentmagnet in the charger or dock using a magnetometer. Alternatively, thebiometric monitoring device may detect proximity to the charger bymeasuring the Received Signal Strength Indication (RSSI) of a wirelesssignal from the charger or dock, or, in some embodiments, by recognizingan NFC or RFID tag associated with the charger or dock. Theelectromagnet could be reversed, creating a force that repels the devicefrom the charging cable or dock either when the device doesn't need tobe charged, synced, or when it has completed syncing or charging. Insome embodiments, the charger or dock may include the electromagnet andmay be configured (e.g., a processor in the charger or dock may beconfigured via program instructions) to turn the electromagnet on when abiometric monitoring device is connected for charging (the electromagnetmay normally be left on such that a biometric monitoring device that isplaced on the charger is drawn against the charger by the electromagnet,or the electromagnet may be left off until the charger determines that abiometric monitoring device has been placed on the charger, e.g.,through completion of a charging circuit, recognition of an NFC tag inthe biometric monitoring device, etc., and then turned on to draw thebiometric monitoring device against the charger. Upon completion ofcharging (or of data transfer, if the charger is actually a datatransfer cradle or a combined charger/data transfer cradle), theelectromagnet may be turned off (either temporarily or until thebiometric monitoring device is again detected as being placed on thecharger) and the biometric monitoring device may stop being drawnagainst the charger. In such embodiments, it may be desirable to orientthe interface between the biometric monitoring device and the chargersuch that, in the absence of a magnetic force generated by theelectromagnet, the biometric monitoring device would fall off of thecharger or otherwise shift into a visibly different position from thecharging position (to visually indicate to a user that charging or datatransfer is complete).

Sensor Use in Data Transfer

In some implementations, biometric monitoring devices may include acommunications interface that may switch between two or more protocolsthat have different data transmission rates and different powerconsumption rates. Such switching may be driven by data obtained fromvarious sensors of the biometric monitoring device. For example, ifBluetooth is used, the communications interface may switch between usingBluetooth base rate/enhanced data rate (BR/EDR) and Bluetooth low energy(BLE) protocols responsive to determinations made based on data from thesensors of the biometric monitoring device. For example, thelower-power, slower BLE protocol may be used when sensor data fromaccelerometers in a biometric monitoring device indicates that thewearer is asleep or otherwise sedentary. By contrast, the higher-power,faster BR/EDR protocol may be used when sensor data from theaccelerometers in a biometric monitoring device indicates that thewearer is walking around. Such adaptive data transmission techniques andfunctionality are discussed further in U.S. Provisional PatentApplication No. 61/948,468, filed Mar. 5, 2014, which was previouslyincorporated herein by reference in the “Cross-Reference to RelatedApplications” section and which is again hereby incorporated byreference with respect to content directed at adaptive data transferrates in biometric monitoring devices.

Such communication interfaces may also serve as a form of sensor for abiometric monitoring device. For example, a wireless communicationsinterface may allow a biometric monitoring device to determine thenumber and type of devices that are within range of the wirelesscommunications interface. Such data may be used to determine if thebiometric monitoring device is in a particular context, e.g., indoors,in a car, etc., and to change its behavior in various ways in responseto such a determination. For example, as discussed in U.S. ProvisionalPatent Application No. 61/948,468 (incorporated by reference above),such contexts may be used to drive the selection of a particularwireless communications protocol to use for wireless communications.

Configurable App Functionality

In some embodiments, biometric monitoring devices of the presentdisclosure may include a watch-like form factor and/or a bracelet,armlet, or anklet form factor and may be programmed with “apps” thatprovide specific functionality and/or display specific information. Appsmay be launched or closed by a variety of means including, but notlimited to, pressing a button, using a capacitive touch sensor,performing a gesture that is detected by an accelerometer, moving to aspecific location or area detected by a GPS or motion sensor,compressing the biometric monitoring device body (thereby creating apressure signal inside the device that may be detected by an altimeterinside the biometric monitoring device), or placing the biometricmonitoring device close to an NFC tag that is associated with an app orset of apps. Apps may also be automatically triggered to launch or closeby certain environmental or physiological conditions including, but notlimited to, detection of a high heart rate, detection of water using awet sensor (to launch a swimming application, for example), a certaintime of day (to launch a sleep tracking application at night, forexample), a change in pressure and motion characteristic of a planetaking off or landing to launch and close an “airplane” mode app. Appsmay also be launched or closed by meeting multiple conditionssimultaneously. For example, if an accelerometer detects that a user isrunning and the user presses a button, the biometric monitoring devicemay launch a pedometer application, an altimeter data collectionapplication, and/or display. In another case where the accelerometerdetects swimming and the user presses the same button, it may launch aswimming lap-counting application.

In some embodiments, the biometric monitoring device may have aswim-tracking mode that may be launched by starting a swimming app. Inthis mode, the biometric monitoring device's motion sensors and/ormagnetometer may be used to detect swim strokes, classify swim stroketypes, detect swimming laps, and other related metrics such as strokeefficiency, lap time, speed, distance, and calorie burn. Directionalchanges indicated by the magnetometer may be used to detect a diversityof lap turn methods. In a preferred embodiment, data from a motionsensor and/or pressure sensor may be used to detect strokes.

In another embodiment, a bicycling app may be launched by moving thebiometric monitoring device within proximity of an NFC or RFID tag thatis located on the bicycle, on a mount on the bicycle, or in a locationassociated with a bicycle including, but not limited to, a bike rack orbike storage facility. (See, for example, FIG. 17). The app launched mayuse a different algorithm than is normally used to determine metricsincluding, but not limited to, calories burned, distance travelled, andelevation gained. The app may also be launched when a wireless bikesensor is detected including, but not limited to, a wheel sensor, GPS,cadence sensor, or power meter. The biometric monitoring device may thendisplay and/or record data from the wireless bike sensor or bikesensors.

Additional apps include, but are not limited to, a programmable orcustomizable watch face, stop watch, music player controller (e.g., mp3player remote control), text message and/or email display or notifier,navigational compass, bicycle computer display (when communicating witha separate or integrated GPS device, wheel sensor, or power meter),weight-lifting tracker, sit-up reps tracker, pull up reps tracker,resistance training form/workout tracker, golf swing analyzer, tennis(or other racquet sport) swing/serve analyzer, tennis game swingdetector, baseball swing analyzer, ball throw analyzer (e.g., football,baseball), organized sports activity intensity tracker (e.g., football,baseball, basketball, volleyball, soccer), disk throw analyzer, foodbite detector, typing analyzer, tilt sensor, sleep quality tracker,alarm clock, stress meter, stress/relaxation biofeedback game (e.g.,potentially in combination with a mobile phone that provides auditoryand/or visual cues to train user breathing in relaxation exercises),teeth brushing tracker, eating rate tracker (e.g., to count or track therate and duration by which a utensil is brought to the mouth for foodintake), intoxication or suitability to drive a motor vehicle indicator(e.g., through heart rate, heart rate variability, galvanic skinresponse, gait analysis, puzzle solving, and the like), allergy tracker(e.g., using galvanic skin response, heart rate, skin temperature,pollen sensing and the like (possibly in combination with externalseasonal allergen tracking from, for instance, the internet and possiblydetermining the user's response to particular forms of allergen, e.g.,tree pollen, and alerting the user to the presence of such allergens,e.g., from seasonal information, pollen tracking databases, or localenvironmental sensors in the biometric monitoring device or employed bythe user), fever tracker (e.g., measuring the risk, onset, or progressof a fever, cold, or other illness, possibly in combination withseasonal data, disease databases, user location, and/or user providedfeedback to assess the spread of a particular disease (e.g., flu) inrelation to a user, and possibly prescribing or suggesting theabstinence of work or activity in response), electronic games, caffeineaffect tracker (e.g., monitoring the physiologic response such as heartrate, heart rate variability, galvanic skin response, skin temperature,blood pressure, stress, sleep, and/or activity in either short term orlong term response to the intake or abstinence of coffee, tea, energydrinks and/or other caffeinated beverages), drug affect tracker (e.g.,similar to the previously mentioned caffeine tracker but in relation toother interventions, whether they be medical or lifestyle drugs such asalcohol, tobacco, etc.), endurance sport coach (e.g., recommending orprescribing the intensity, duration, or profile of arunning/bicycling/swimming workout, or suggesting the abstinence ordelay of a workout, in accordance with a user specified goal such as amarathon, triathlon, or custom goal utilizing data from, for instance,historical exercise activity (e.g., distance run, pace), heart rate,heart rate variability, health/sickness/stress/fever state), weightand/or body composition, blood pressure, blood glucose, food intake orcaloric balance tracker (e.g., notifying the user how many calories hemay consume to maintain or achieve a weight), pedometer, and nail bitingdetector. In some cases, the apps may rely solely on the processingpower and sensors of the present disclosure. In other cases, the appsmay fuse or merely display information from an external device or set ofexternal devices including, but not limited to, a heart rate strap, GPSdistance tracker, body composition scale, blood pressure monitor, bloodglucose monitor, watch, smart watch, mobile communication device such asa smart phone or tablet, or server.

In one embodiment, the biometric monitoring device may control a musicplayer on a secondary device. Aspects of the music player that may becontrolled include, but are not limited to, the volume, selection oftracks and/or playlists, skipping forward or backward, fast forwardingor rewinding of tracks, the tempo of the track, and the music playerequalizer. Control of the music player may be via user input orautomatic based on physiological, environmental, or contextual data. Forexample, a user may be able to select and play a track on their smartphone by selecting the track through a user interface on the biometricmonitoring device. In another example, the biometric monitoring devicemay automatically choose an appropriate track based on the activitylevel of the user (the activity level being calculated from biometricmonitoring device sensor data). This may be used to help motivate a userto maintain a certain activity level. For example, if a user goes on arun and wants to keep their heart rate in a certain range, the biometricmonitoring device may play an upbeat or higher tempo track if theirheart rate is below the range which they are aiming for.

Automated Functions Triggered by User's Activity

Sleep Stage Triggered Functionality

Sleep stages can be monitored through various biometric signals andmethods disclosed herein, such as heart rate, heart rate variability,body temperature, body motions, ambient light intensity, ambient noiselevel, etc. Such biometrics may be measured using optical sensors,motion sensors (accelerometers, gyroscopic sensors, etc.), microphones,and thermometers, for example, as well as other sensors discussedherein.

The biometric monitoring device may have a communication module as well,including, but not limited to, Wi-Fi (802.xx), Bluetooth (Classic, lowpower), or NFC. Once the sleep stages are estimated, the sleep stagesmay be transmitted to a cloud-based system, home server, or main controlunit that is connected to communication-enabled appliances (with Wi-Fi,Bluetooth, or NFC) wirelessly. Alternatively, the biometric monitoringdevice may communicate directly with the communication-enabledappliances. Such communication-enabled appliances may include, forexample, kitchen appliances such as microwaves, ovens, coffeegrinders/makers, toasters, etc.

Once the sleep stages indicate that it is close the time for the user towake up, the biometric monitoring device may send out a trigger to theappliances that the user has indicated should be operated automatically.For example, the coffee grinder and maker may be caused to start makingcoffee, and the toaster may be caused to start warming up bread. Themicrowave oven may be caused to start cooking oatmeal or eggs as well,and electric kettle to start boiling water. So long as the ingredientsare appropriately prepared, this automated signal may triggerbreakfast-cooking.

Alertness Detection

Alertness, e.g., a low alertness may correlate with a person beingdrowsy, may also be detected from the biometrics listed above, and maybe used to trigger an appliance such as a coffee maker to start brewingcoffee automatically.

Hydration

[1] The portable biometric monitoring device in combination with anactivity level tracker may submit the user's activity level to acloud-based system, home server, main control unit, or appliancesdirectly. This may trigger some actions of the appliances, especiallyrelated to hydration, such as starting the ice cube maker of arefrigerator, or lowering operating temperature of a water purifier.

Power Saving

Many appliances typically operate in alow-power idle state that consumespower. Using aggregated information of the user's biometric signals,communication-enabled appliances may be caused to go into a super-lowpower mode. For example, a water dispenser at home may shut itself downinto a super-low-power mode when the user is asleep or out for work, andmay start cooling/heating water once the user's activity at home isexpected.

Restaurant Recommendation System Based on Location and Activity

Aggregation of real-time biometric signals and location information maybe used to create an educated-guess on one or multiple users' needs fora given time, e.g., ionized drink. Combining this guessed need withhistorical user data on the user's activity levels, activity types,activity time, and activity durations, as well as food intake datalogged by the users, an app on a smart phone and/or smart watch mayrecommend a restaurant that would meet the user's life-style and currentneed.

For example, a user who just finished a six mile circuit may launch thisapp. The app may know that this person maintained a high activity levelfor the past hour, and thus determine that the person may be dehydrated.From the historical user data, the app may also know, for example, thatthe user's diet is heavy on vegetables but low in sugar. With anoptimization algorithm that considers the user's current location, priceranges, and other factors mentioned above, the app may recommend arestaurant that offers smoothies, for example.

Blood Glucose Level and Heart Rate

Biometric monitoring devices that continuously measure biometric signalsmay provide meaningful information on preconditions of, progresstowards, and recoveries from diseases. Such biometric monitoring devicesmay have sensors and run algorithms accordingly to measure and calculatebiometric signals such as heart rate, heart rate variability, stepstaken, calories burned, distance traveled, weight and body fat, activityintensity, activity duration and frequency, etc. In addition to themeasured biometric signals, food intake logs provided by users may beused.

In one embodiment, a biometric monitoring device may observe heart rateand its changes over time, especially before and after a food intakeevent or events. It is known that heart rate is affected by blood sugarlevel, whereas it is well known that high blood sugar level is apre-diabetic condition. Thus, mathematical models that describe therelation between time elapsed (after food intake) and blood sugar levelmay be found via statistical regression, where data are collected fromnormal, pre-diabetic, and diabetic individuals to provide respectivemathematical models. With the mathematical models, one may predictwhether an individual with specific heart rate patterns is healthy,pre-diabetic, or diabetic.

Knowing that many heart failures are associated with pre-diabetic ordiabetic conditions, it is possible to further inform users of biometricmonitoring devices with possible heart failures, e.g., coronary heartdisease, cerebrovascular disease and peripheral vascular disease etc.,of such risks based on their biometric data.

Users' activity intensity, type, duration, and frequency may also betaken into account, when developing the mathematical models, as anargument that controls “probability” of the disease onset, usingrecommended exercise guidelines such as guidelines provided by AmericanHeart Association (http://www.heart.org/). Many guidelines on nutritionand weight management are also available in academia and to the generalpublic to prevent cardiovascular and diabetic disease. Such guidelinesmay be incorporated into the mathematical models with the user dataaccumulated over time, such as ingredients of the food that the usersconsumed, and weight and body fat trends.

If users have set their family members as their friends on a socialnetwork site, which stores and displays biometric data, the likelihoodof the family members getting a disease may also be analyzed and theusers informed of the results.

In addition to informing users regarding a potential development ofdisease, recommended life-style including exercise regime and recipeswith healthier ingredients and methods of preparation may be provided tothe users.

Sport Metric Acquisition Using a Sensor Device

In some embodiments, a sensor may be mounted on a racket, e.g., tennisracket, to help to measure the different strokes of the player. This maybe applicable to most, if not all, racket sports including, but notlimited to, tennis, racquetball, squash, table tennis, badminton,lacrosse, etc., as well as sports played with a bat like baseball,softball, cricket, etc. Similar techniques may also be used to measuredifferent aspects of golf. Such a device can be mounted on the base ofthe racket, on the handle or on the shock absorber typically mounted onthe strings. This device may have various sensors like an accelerometer,gyroscope, magnetometer, strain sensor, and/or microphone. The data fromthese sensors may either be stored locally or transmitted wirelessly toa host system on a smartphone or other wireless receiver.

In some embodiments of a biometric monitoring device, a wrist mountedbiometric monitoring device including an accelerometer, gyroscope,magnetometer, microphone, etc. may perform similar analysis of theuser's game or motions. This biometric monitoring device may take theform of a watch or other band worn on the user's wrist. Racket- orbat-mounted sensors that measure or detect the moment of impact betweenthe bat or racket and the ball and wirelessly transmit such data to thewrist-mounted biometric monitoring device may be used to improveaccuracy of such algorithms by accurately measuring the time of impactwith the ball.

Both wrist and racket-/bat-mounted devices may help measure differentaspects of the user's game including, but not limited to, stroke-type(forehand, backhand, serve, slice, etc.), number of forehands, number ofbackhands, ball spin direction, topspin, service percentage, angularvelocity of racket head, backswing, shot power, shot consistency, etc.The microphone or the strain sensor may be used in addition to theaccelerometer to identify the moment at which the ball impacts theracket/bat. In cricket and baseball, such a device may measure thebackswing, the angular velocity of the bat at the time of impact, thenumber of shots on the off-side vs. leg-side (cricket). It may alsomeasure the number of swings and misses and the number of defensive vs.offensive strokes. Such a device may also have a wireless transmitter totransmit such statistics in real time to a scoreboard or to individualdevices held by spectators.

The wrist- or racket-mounted device may have a small number of buttons(e.g., two) that may be used by the player to indicate when a volley iswon or when an unforced error occurs. This will allow the algorithm tocalculate the fraction of winners and unforced errors that are forehandsvs. backhands. The algorithm may also keep track of the number of acesvs. double-faults in tennis. If both players use such a system, thesystem may also automatically keep track of the score.

Indirect Metric Estimation

Bicycle computers typically measure a variety of metrics including, butnot limited to, speed, cadence, power, and wind speed. In the case thatthe portable monitoring device does not measure these metrics or is notin communication with devices which may be able to supply these metrics,these and other metrics may be inferred using the sensors that theportable biometric monitoring device does have. In one embodiment, theportable biometric monitoring device may measure heart rate. It may usethis measurement to infer/estimate the amount of power that the user isoutputting. Other metrics such as the user's age, height, and weight mayhelp inform the power measurement. Additional sensor data such asGPS-measured speed, altitude gain/descent, bicycle attitude (so as themeasure the incline or decline of a slope), and accelerometer signalsmay be used to further inform the power estimate. In one embodiment, anapproximately linear relationship between heart rate and power outputmay be used to calculate the user's power output.

In one embodiment, a calibration phase may occur where the user takesdata from the portable biometric monitoring device and a secondarydevice that may be used during calibration as a baseline but not be usedat a later time (e.g., a power meter). This may allow a relationshipbetween sensor data measured by the portable monitoring device andsensor data measured by the secondary device data to be determined. Thisrelationship may then be used when the secondary device is not presentto calculate estimated values of data that is explicitly provided by thesecondary device but not by the biometric monitoring device.

Activity Based Automatic Scheduling

In one embodiment, the day's travel requirements (to work, from work,between meetings) may be scheduled for the user based on the informationin their calendar (or emails or text messages etc.), with the aim ofmeeting daily activity goal(s) or long term activity goal(s). The user'shistorical data may be used to help plan both meeting the goal(s) andalso the transit time required. This feature may be combined withfriends or colleagues. The scheduling may be done such that a user maymeet a friend along the way as they walk to work, or meet a colleague onthe way to a meeting (the user might need to set a rendezvous point,though). If there is real-time communication between biometricmonitoring devices of the user and the user's friend, the user may bedirected to walk a longer route if data from the friend's biometricmonitoring device indicates that their friend is running late.

In another embodiment, walking/running/fitness routes may be suggestedto the user based (in whole or in part) on their proximity to the user.The data for such recommendations could also or additionally be based onGPS info from other users. If there is real-time communication, the usermay be directed to a busy route or a quiet route as preferred. Knowingheart rate and basic fitness information about other users may allow thesystem to suggest a route to match a user's fitness level and thedesired exercise/exertion level. Again this information may be used forplanning/guiding a user to longer term activity/fitness goals.

Location/Context Sensing and Applications

Through one or more methods, embodiments of the biometric monitoringdevices disclosed herein may have sensors that can determine or estimatethe location and or context (e.g. in a bus, at home, in a car) of thebiometric monitoring device. Purpose-built location sensors such as GPS,GLONASS, or other GNSS (Global Navigation Satellite System) sensors maybe used. Alternatively, location may be inferred, estimated or guessedusing less precise sensors. In some embodiments in which it is difficultto know the user's location, user input may aid in the determination oftheir location and or context. For example, if sensor data makes itdifficult to determine if a user was in a car or a bus, the biometricmonitoring device or a portable communication device in communicationwith the biometric monitoring device or a cloud server which is incommunication with the biometric monitoring device may present a queryto the user asking them if they took the bus today or took a car.Similar queries may occur for locations other than vehicular contexts.For example, if sensor data indicate that the user completed a vigorousworkout, but there is no location data that indicates that the user wentto a gym, the user may be asked if they went to the gym today.

Vehicular Transportation Detection

In some embodiments, sensors of the biometric monitoring device and/or aportable electronic device in communication with the biometricmonitoring device and/or a server which communicates with the biometricmonitoring device may be used to determine what type of vehicle (if any)the user is, or was, in. Note that in the embodiments below, a sensor inone or more biometric monitoring devices and/or portable electronicdevices may be used to sense the relevant signal. Also note that whilespecific network protocols such as WiFi or Bluetooth may be used in thefollowing descriptions, one or more alternative protocols such as RFID,NFC, or cellular telephony may also be used.

In one embodiment, the detection of a Bluetooth device associated with avehicle may be used to infer that the user is in a vehicle. For example,a user may have a car that has a Bluetooth multimedia system. When theuser gets close enough to their car for a long enough period of time,the sensor device may recognize the Bluetooth identification of themultimedia system and assume that the user is in the car. Data fromother sensors may be used to corroborate the assumption that the user isin the vehicle. Examples of data or signals from other sensors that maybe used to confirm that the user is in a car include a GPS speedmeasurement that is higher than 30 mph and accelerometer signals thatare characteristic of being in a car. Information intrinsic to theBluetooth ID may be used to determine that it is a Wi-Fi router of avehicle or type of vehicle. For example, the Bluetooth ID of a router ina car may be “Audi In-Car Multimedia.” The keyword “Audi” or “Car” maybe used to guess that the router is associated with a vehicle type of“car.” Alternatively, a database of Bluetooth ID's and their associatedvehicles may be used.

In one embodiment, a database of Bluetooth ID's and their associatedvehicles may be created or updated by the user of a biometric monitoringdevice or through portable communication device data. This may be donewith and/or without the aid of user input. In one embodiment if abiometric monitoring device can determine whether or not it is in avehicle, vehicle type, or specific vehicle without the use of BluetoothID, and it encounters a Bluetooth ID that moves with the vehicle, it maysend the Bluetooth ID and information regarding the vehicle to a centraldatabase to be catalogued as a Bluetooth ID that corresponds with avehicle. Alternatively, if a user inputs information about the vehiclethey are in or were in at a previous point in time and there is aBluetooth ID that was encountered during or close to the time that theuser indicated they were in the vehicle, the Bluetooth ID and vehicleinformation may be sent to a central database and associated with oneanother.

In another embodiment, the detection of a Wi-Fi device associated with avehicle may be used to infer that the user is in that vehicle or type ofvehicle. Some trains, buses, airplanes, cars, and other vehicles haveWi-Fi routers in them. The SSID of the router may be detected and usedto infer or aid an inference that a user is in a specific vehicle ortype of vehicle.

In one embodiment, a database of SSID's and their associated vehiclesmay be created or updated with the user of a biometric monitoring deviceor through portable communication device data. This may be done withand/or without the aid of user input. In one embodiment, if a biometricmonitoring device can determine whether or not it is in a vehicle,vehicle type, or specific vehicle without the use of an SSID, and itencounters an SSID that moves with the vehicle, the biometric monitoringdevice may send the SSID and information regarding the vehicle to acentral database to be catalogued as an SSID that corresponds with avehicle. Alternatively, if a user inputs information about the vehiclethey are in or were in at a previous point in time and there is an SSIDthat was encountered during or close to the time that the user indicatedthey were in the vehicle, the SSID and vehicle information may be sentto a central database and associated with one another.

In another embodiment of a biometric monitoring device, location sensorsmay be used to determine the track of a user. This track may then becompared to a database of routes for different modes of transit. Modesof transit may include, but are not limited to walking, running, biking,driving, taking a bus, taking a train, taking a tram, taking the subway,and/or motorcycling. If the user's track corresponds well with a routeof a specific mode of transit, it may be assumed that the user used thatmode of transit for the period of time that it took them to traverse theroute. Note that the speed with which the route or sections of the routewere completed may improve the guess of the mode of transit. Forexample, a bus and a car may both be able to take the same route, butthe additional stopping of the bus at bus stops may allow the device todetermine that the user was taking a bus rather than a car. Similarly,the discrimination between biking and driving a route may be aided bythe typical difference of speed between the two. This difference inspeed may also depend on the time of day. For example, some routes maybe slower by car during rush hour.

In another embodiment, a biometric monitoring device may be able todetect that the user is in or near a vehicle based on measurements ofthe magnetic field of vehicle. In some embodiments, the magnetic fieldsignature of a location typically associated with the vehicle (e.g.,train station, subway station, bus stop, car garage) may also be used toinfer that the user is currently in, will be, or has been in a vehicle.The magnetic field signature may be time invariant or time varying.

If it is determined that the user was indeed in a vehicle for a periodof time, other metrics about the user may be modified to reflect such astatus. In the case that the biometric monitoring device and/or portableelectronic device can measure activity metrics such as steps taken,distance walked or run, altitude climbed, and/or calories burned, thesemetrics may be modified based on information about vehicular travel. Ifany steps taken or altitude climbed were incorrectly logged during thetime that the user is in a vehicle, they may be removed from the log ofmetrics about the user. Metrics derived from the incorrectly loggedsteps taken or altitude climbed such as distance travelled and caloriesburned may also be removed from the log of metrics about the user. Inthe case that it can be determined in real-time or near real-timewhether or not the user is in a vehicle, the sensors detecting metricswhich should not be measured while in a vehicle (e.g. steps taken,stairs climbed) may be turned off or algorithms which are used tomeasure these metrics may be turned off to prevent incorrectly loggedmetrics (as well to save power). Note that metrics regarding vehicle usesuch as type of vehicle taken, when it was taken, which route was taken,and how long the trip took may be recorded and used later to present theuser with this data and/or to correct other activity and physiologicalmetrics about the user.

Location Sensing Using Bluetooth

Methods similar to those described above may also be used by a biometricmonitoring device to determine when the user comes into proximity ofstatic locations. In one embodiment, Bluetooth ID's from computers(e.g., tablet computers) at restaurants or stores may be used todetermine the user's location. In another embodiment, semi-fixedBluetooth ID's from portable communication devices (e.g., smartphones)may be used to determine a user's location. In the case of semi-fixedBluetooth ID sources, multiple Bluetooth ID's may be need to reach anacceptable level of confidence of the location of the user. For example,a database of Bluetooth ID's of the coworkers of a user may be created.If the user is within range of several of these Bluetooth ID's duringtypical working hours, it may be assumed that the user is at work. Thedetection of other Bluetooth ID's may also be used to record when twousers meet up. For example, it may be determined that a user went for arun with another user by analyzing pedometer data and Bluetooth ID's.Similar such concepts are discussed in further detail in U.S.Provisional Patent Application No. 61/948,468, filed Mar. 5, 2014, andpreviously incorporated by reference with regard to such concepts.

Uncertainty Metric for GPS Based on Location

When fusing sensor signals with GPS signal to estimate informativebiometrics, such as steps, live pace, speed, or trajectory of trips,quality of the GPS signal is often very informative. However, GPS signalquality is known to be time-varying, and one of the factors that affectsthe signal quality is environmental surroundings.

Location information may be used to estimate GPS signal quality. Aserver may store a map of area types, where the area types arepre-determined by number and kind of objects that deteriorate GPSsignals. The types may, for example, be: large building area, smallbuilding area, open area, side-by-water area, and forested area. Thesearea types are then queried when GPS sensor gets turned on with its veryfirst few location estimates, which are expected to be rough andinaccurate. With the rough GPS estimates of the location, possible typesof areas may be returned, and these area types may then be taken intoaccount in the calculation of the GPS signal quality and reliability.

For example, if a user is in or near an urban canyon (an area surroundby tall buildings) such as downtown San Francisco, a low certainty maybe associated with any GNSS location measurements. This certainty may beused later by algorithms that attempt to determine the user's track,speed, and/or elevation based on, at least in part, GPS data.

In one embodiment, a database of location and GPS signal quality may becreated automatically using data from one or more GNSS sensors. This maybe automatically performed by comparing the GNSS tracks with a map ofstreets and seeing when the GNSS sensors show characteristics of a usertravelling along a street (e.g., having a speed of 10 mph or higher),but their track is not located on a road. The database of GPS certaintybased on approximate location may also be inferred from maps showingwhere there are tall buildings, canyons, or dense forests.

Location Sensing Using Vehicular GNSS and/or Dead Reckoning

Many vehicles have integrated GNSS navigation systems. Users of vehiclesthat don't have integrated GNSS navigations systems often buy a GNSSnavigation system for their car that is typically mountednon-permanently in the driver's field of view. In one embodiment, aportable biometric monitoring device may be able to communicate with thevehicle's GNSS system. In the case where the portable biometricmonitoring device is also used to track location, it may receivelocation information from the vehicle GNSS. This may enable thebiometric monitoring device to turn off its own GNSS sensor (in the casethat it has one), therefore reducing its power consumption.

In addition to GNSS location detection, a vehicle may be able totransmit data about its steering wheel orientation and/or itsorientation with respect to the earth's magnetic field in addition toits speed as measured using the tire size and tire rotational velocity.This information may be used to perform dead-reckoning to determine atrack and/or location in the case that the vehicle does not have a GNSSsystem or the vehicle's GNSS system cannot get a reliable locationmeasurement. Dead-reckoning location information may supplement GNSSsensor data from the biometric monitoring device. For example, thebiometric monitoring device may reduce the frequency with which itsamples GNSS data and fill in the gap between GNSS location data withlocations determined through dead reckoning.

Step Counter Data Fusion with Satellite-Based Location Determination

In some implementations of a biometric monitoring device, data fromvarious different sensors may be fused together to provide new insightsas to activities of the wearer of the biometric monitoring device. Forexample, data from an altimeter in the biometric monitoring device maybe combined with step count data obtained by performing peak detectionanalysis on accelerometer data from an accelerometer of the biometricmonitoring device to determine when the wearer of the biometricmonitoring device is, for example, climbing stairs or walking uphill (asopposed to riding an elevator or an escalator or walking across flatground).

In another example of sensor data fusion, data from a step counter suchas that discussed above may be combined with distance measurementsderived from GPS data to provide a refined estimate of total distancetraveled within a given window. For example, GPS-based distance or speeddata may be combined with step-counter-based distance or speed (usingsteps taken multiplied by stride length, for example) using a Kalmanfilter in order to obtain a refined distance estimate that may be moreaccurate than either the GPS-based distance or speed measurement or thestep-counter-based distance or speed measurement alone. In anotherimplementation, a GPS-based distance measurement may be filtered using asmoothing constant that is a function of the step rate as measured by,for example, an accelerometer. Such implementations are discussedfurther in U.S. Provisional Patent Application No. 61/973,614, filedApr. 1, 2014, which was previously incorporated herein by reference inthe “Cross-Reference to Related Applications” section and which is againhereby incorporated by reference with respect to content directed atdistance or speed estimation refinement using data from satellite-basedlocation systems and step count sensors.

Biometric and Environmental/Exercise Performance Correlation

Some embodiments of portable monitoring devices described herein maydetect a variety of data including biometric data, environmental data,and activity data. All of this data may be analyzed or presented to auser to facilitate analysis of or correlation between two or more typesof data. In one embodiment, a user's heart rate may be correlated to carspeed, biking speed, running speed, swimming speed or walking speed. Forexample, the user may be presented with a graph that plots biking speedon the X axis and heart rate on the Y axis. In another example, a user'sheart rate may be correlated to music that they were listening to. Thebiometric monitoring device may receive data regarding what music theuser was listening to through a wireless connection (e.g., Bluetooth) toa car radio. In another embodiment, the biometric monitoring device mayalso function as a music player itself, and therefore can record whichsong was played when.

UV Exposure Detection

In one embodiment, the biometric monitoring device may have the abilityto monitor an individual's exposure to UV radiation. UVA and UVB may bemeasured with one or multiple sensors. For example, a photodiode havinga bandpass filter which passes only UVA may detect UVA exposure and aphotodiode having a bandpass filter which passes only UVB may detect UVBexposure. The user's skin pigmentation may also be measured using acamera or reflectometer (light emitter and light detector whichdetermines the efficiency with which light is reflected off the skin).Using UVA, UVB, and skin pigmentation data, the biometric monitoringdevice may provide a user with information regarding the amount of UVexposure they have been subjected to. The biometric monitoring devicemay also provide estimates or alarms regarding over exposure to UV,potential for sunburn, and potential for increasing their risk of skincancer.

Screen Power Saving Using User Presence Sensors

The portable biometric monitoring device may have one or more a displaysto present information to the user. In one embodiment sensors on thebiometric monitoring device may determine the user is using thebiometric monitoring device and/or wearing the biometric monitoringdevice to determine the state of the display. For example, a biometricmonitoring device having a PPG sensor may use the PPG sensor as aproximity sensor to determine when the user is wearing the biometricmonitoring device. If the user is wearing the biometric monitoringdevice, the state of the screen (e.g. a color LCD screen) may be changedto “on” or “standby” from its typical state of being off.

Power Conservation with Respect to Satellite-Based LocationDetermination Systems

In some implementations, certain systems included in a biometricmonitoring device may consume relatively large amounts of power comparedto other systems in the biometric monitoring device. Due to the smallspace constraints of many biometric monitoring devices, this mayseriously affect overall battery charge life for the biometricmonitoring device. For example, in some biometric monitoring devices, asatellite-based location determination system may be included. Each timethe satellite-based location determination system is used to obtain aposition fix using data from the GPS satellite constellation, it usespower drawn from the biometric monitoring device battery. The biometricmonitoring device may be configured to alter the frequency with whichthe satellite-based location determination system obtains a location fixbased on data from one or more sensors of the biometric monitoringdevice. This adaptive location fix frequency functionality may helpconserve power while still allowing the satellite-based locationdetermination system to provide location fixes at useful intervals (whenappropriate).

For example, if a biometric monitoring device has an ambient lightsensor, data from the ambient light sensor may be used to determinewhether the lighting conditions indicate that the biometric monitoringdevice is likely indoors as opposed to outdoors. If indoors, thebiometric monitoring device may cause the location fix frequency to beset to a level that is lower than the location fix frequency that may beused when the lighting conditions appear to indicate that the biometricmonitoring device is outdoors. This has the effect of decreasing thenumber of location fixes that are attempted when the biometricmonitoring device is indoors and thus less likely to obtain a goodlocation fix using a satellite-based location determination system.

In another example, if motion sensors of the biometric monitoring deviceindicate that the wearer of the biometric monitoring device issubstantially stationary, e.g., sleeping or generally not moving morethan a few feet every minute, the location fix frequency of thesatellite-based location determination system may be set to a lowerlevel than if the motion sensors indicate that the wearer of thebiometric monitoring device is in motion, e.g., walking or running fromone location to another, e.g., moving more than a few feet.

In yet another example, the biometric monitoring device may beconfigured to determine if the biometric monitoring device is actuallybeing worn by a person—if not, the biometric monitoring device may setthe location fix frequency to a lower level than if the biometricmonitoring device is actually being worn. Such determinations regardingwhether or not the biometric monitoring device is being worn may bemade, for example, when motion data collected from motion sensors of thebiometric monitoring device indicate that the biometric monitoringdevice is substantially immobile, e.g., not even demonstrating smallmovements experienced by biometric monitoring devices when the wearer issleeping or sedentary, or when data, for example, from a pulse waveformsensor indicates that no heart rate is detected. For optical pulsewaveform sensors, if there is little or no change in the amount of lightdetected by the light detection sensor when the light source is turnedon and off, this may be indicative of the fact that the pulse waveformsensor is not pressed against a person's skin and that, by inference,the biometric monitoring device is not being worn. Such adaptivesatellite-based location determination system fix frequency concepts arediscussed in more detail in U.S. Provisional Patent Application No.61/955,045, filed Mar. 18, 2014, which was previously incorporatedherein by reference in the “Cross-Reference to Related Applications”section and which is again hereby incorporated by reference with respectto content directed at power conservation in the context ofsatellite-based location determination systems.

It is to be understood that biometric monitoring devices, in addition toincluding the features discussed below in more detail, may also includeone or more features or functionalities discussed above or discussed inthe various applications incorporated by reference in the abovediscussion. Such implementations are to be understood as being withinthe scope of this disclosure.

There are many concepts and embodiments described and illustratedherein. While certain embodiments, features, attributes, and advantageshave been described and illustrated herein, it should be understood thatmany others, as well as different and/or similar embodiments, features,attributes and advantages are apparent from the description andillustrations. As such, the above embodiments are merely provided by wayof example. They are not intended to be exhaustive or to limit thisdisclosure to the precise forms, techniques, materials and/orconfigurations disclosed. Many modifications and variations are possiblein light of this disclosure. It is to be understood that otherembodiments may be utilized and operational changes may be made withoutdeparting from the scope of the present disclosure. As such, the scopeof the disclosure is not limited solely to the description above becausethe descriptions of the above embodiments have been presented for thepurposes of illustration and description.

Importantly, the present disclosure is neither limited to any singleaspect nor embodiment, nor to any combinations and/or permutations ofsuch aspects and/or embodiments. Moreover, each of the aspects of thepresent disclosure, and/or embodiments thereof, may be employed alone orin combination with one or more of the other aspects and/or embodimentsthereof. For the sake of brevity, many of those permutations andcombinations will not be discussed and/or illustrated separately herein.

1. A method for calibrating a system comprising a calibration bloodpressure measuring device, one or more sensors configured to measurepulse transit time, a memory, and one or more processors, the methodcomprising: (a) taking at least one measurement of a blood pressure of aperson with the calibration blood pressure device when the person isperforming a Valsalva maneuver, wherein the calibration blood pressuremeasuring device does not rely on pulse transit time to measure theblood pressure; (b) obtaining proximal pulse wave data and distal pulsewave data from the one or more sensors when the person is performing theValsalva maneuver; (c) obtaining, by using the one or more processors,at least one pulse transit time using the proximal pulse wave data andthe distal pulse wave data obtained when the person is performing theValsalva maneuver; (d) obtaining, by using the one or more processors,at least one calibration data point corresponding to the at least onemeasurement of the blood pressure and the at least one pulse transittime; and (e) fitting, by using the one or more processors, a model totwo or more data points comprising the at least one calibration datapoint, wherein the model relates the blood pressure of the person to thepulse transit time of the person.
 2. The method of claim 1, furthercomprising: obtaining PPG data from the person during a time period whenthe Valsalva maneuver is performed; and initiating (a) and (b) based ondetermining, by the one or more processors, that an amplitude of the PPGdata has decreased in the time period by more than a threshold value. 3.The method of claim 1, further comprising: measuring heart rate of theperson during a time period when the Valsalva maneuver is performed; andinitiating (a) and (b) based on determining, by the one or moreprocessors, that the heart rate of the person has increased in the timeperiod by more than a threshold value.
 4. The method of claim 1, furthercomprising: receiving a user request to calibrate the system; andinitiating (a) and (b) based on the user request.
 5. The method of claim1, further comprising: repeating (a)-(d) one or more times.
 6. Themethod of claim 1, further comprising, after (e): obtaining a testproximal pulse wave data and a test distal pulse wave data from theperson; obtaining a test pulse transit time or a value derived therefromusing the test proximal pulse wave data and the test distal pulse wavedata; and applying the test pulse transit time or the value derivedtherefrom to the model to obtain an estimate of blood pressure.
 7. Themethod of claim 1, wherein the model comprises a linear mathematicalrelationship, a general linear model, a non-linear model, or a neuralnetwork model.
 8. The method of claim 1, wherein the two or more datapoints of (d) comprise a baseline data point.
 9. The method of claim 8,wherein the baseline data point corresponds to: a baseline measurementof a blood pressure of the person obtained when the person is in abaseline state; and a baseline pulse transit time obtained when theperson is in the baseline state.
 10. The method of claim 1, furthercomprising, before (a), providing instructions to the person to instructthe person to initiate a calibration of the system.
 11. The method ofclaim 10, wherein the instructions comprise instructing the person toperform a Valsalva maneuver.
 12. The method of claim 10, wherein theinstructions are provided based on a time schedule.
 13. The method ofclaim 10, wherein the instructions are provided based on a user context.14. The method of claim 13, wherein the user context is selected fromthe group consisting of: the user was at a restaurant, time isapproaching the time that the user typically goes to sleep, the userjust exercised, the user just woke up, the user just ended a commute,significant change has occurred in perfusion properties when stationary,the hand to which one of the sensors is attached is at approximately thesame orientation as before, and any combinations thereof.
 15. The methodof claim 1, further comprising: receiving a user request to calibratethe system; and providing the instructions to the person to instruct theperson to initiate the calibration based on the user request.
 16. Amethod for calibrating a system comprising a calibration blood pressuredevice for measuring blood pressure, one or more sensors configured tomeasure pulse transit time, a temperature sensor, a memory, and one ormore processors, the method comprising: (a) taking at least onemeasurement of a blood pressure of a person with the calibration bloodpressure device when the person is performing the cold pressor maneuver,wherein the calibration blood pressure device does not rely on pulsetransit time to measure the blood pressure; (b) obtaining proximal pulsewave data and distal pulse wave data from the one or more sensors whenthe person is performing the cold pressor maneuver; (c) obtaining, byusing the one or more processors, at least one pulse transit time usingthe proximal pulse wave data and the distal pulse wave data obtainedwhen the person is performing the cold pressor maneuver; (d) obtaining,by using the one or more processors, at least one calibration data pointcorresponding to the at least one measurement of the blood pressure andthe at least one pulse transit time; and (e) fitting, by using the oneor more processors, a model to two or more data points comprising the atleast one calibration data point, wherein the model relates the bloodpressure of the person to the pulse transit time of the person.
 17. Themethod of claim 16, further comprising: repeating (a)-(d) one or moretimes.
 18. The method of claim 16, wherein the two or more data pointsof (d) comprise a baseline data point.
 19. The method of claim 18,wherein the baseline data point comprises: a baseline measurement of ablood pressure of the person obtained when the person is in a baselinestate, or a value derived therefrom; and a baseline pulse transit timeobtained when the person is in the baseline state, or a value derivedtherefrom.
 20. (canceled)
 21. A method for calibrating a systemcomprising an altimeter, a calibration blood pressure device formeasuring blood pressure, one or more sensors for measuring pulsetransit time, a memory, and one or more processors, the methodcomprising: (a) obtaining, by the one or more processors, a measurementof blood pressure of the person with the blood pressure device and a setof altitude data from the altimeter when the person is holding a limb ata position with the system attached to the limb, and wherein thecalibration blood pressure device does not rely on pulse transit time tomeasure blood pressure; (b) obtaining, by the one or more processors, anestimate of hydrostatic pressure from the set of altitude data; (c)obtaining proximal pulse wave data and distal pulse wave data by usingthe one or more sensors when the person is holding the limb at the firstposition; (d) obtaining, by the one or more processors, a pulse transittime using the proximal pulse wave data and the distal pulse wave data;(e) repeating (a)-(d) one or more times when the person is holding thelimb at one or more different positions, thereby obtaining two or morecalibration data points corresponding to two or more measurements ofblood pressure, two or more pulse transit times, and two or moremeasurements of hydrostatic pressure; and (f) fitting, by the controllogic, a model to data points comprising the two or more calibrationdata points, wherein the model relates blood pressure to pulse transittime and hydrostatic pressure. 22-53. (canceled)